Market Insider - Data Science Batch 38¶

Anggota:¶

  1. Achmad Hilman Shadiqin
  2. Figo Akmal Munir
  3. Andreawan Sofian
  4. Nabilah Astiarini
  5. Dzakwan Darussalam
  6. Riyan Maula

customer.png

Table of Contents

  1. Introduction
  2. Imports
  3. EDA + Business Insights
    • 3.1 Descriptive Statistics
    • 3.2 Univariate Analysis
    • 3.3 Multivariate Analysis
    • 3.4 Business Insights
  4. Data Pre-Processing
    • 4.1 Splitting Data
    • 4.2 Handling Missing Values
    • 4.3 Handling Duplicates Values
    • 4.4 Handling Outliers
    • 4.5 Handling Invalid Values
    • 4.6 Feature Extraction
    • 4.7 Feature Encoding
    • 4.8 Feature Transformation
    • 4.9 Feature Selection
    • 4.10 Imbalance Handling
  5. Machine Learning Modelling & Evaluation
    • 5.1 Modeling & Evaluation
      • 5.1.1 Default Parameter
      • 5.1.2 Hyper Tuning Parameter
    • 5.2 Business Simulation & Recommendation
      • 5.1.1 Business Simulation
      • 5.1.2 Business Recommendation

1. Introduction

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Problem Statement¶

A response model can provide a significant boost to the efficiency of a marketing campaign by increasing responses or reducing expenses. The objective is to predict who will respond to an offer for a product or service

Attributes¶

People

  • ID: Customer's unique identifier
  • Year_Birth: Customer's birth year
  • Education: Customer's education level
  • Marital_Status: Customer's marital status
  • Income: Customer's yearly household income
  • Kidhome: Number of children in customer's household
  • Teenhome: Number of teenagers in customer's household
  • Dt_Customer: Date of customer's enrollment with the company
  • Recency: Number of days since customer's last purchase
  • Complain: 1 if customer complained in the last 2 years, 0 otherwise

Products

  • MntWines: Amount spent on wine in last 2 years
  • MntFruits: Amount spent on fruits in last 2 years
  • MntMeatProducts: Amount spent on meat in last 2 years
  • MntFishProducts: Amount spent on fish in last 2 years
  • MntSweetProducts: Amount spent on sweets in last 2 years
  • MntGoldProds: Amount spent on gold in last 2 years

Promotion

  • AcceptedCmp1: 1 if customer accepted the offer in the 1st campaign, 0 otherwise
  • AcceptedCmp2: 1 if customer accepted the offer in the 2nd campaign, 0 otherwise
  • AcceptedCmp3: 1 if customer accepted the offer in the 3rd campaign, 0 otherwise
  • AcceptedCmp4: 1 if customer accepted the offer in the 4th campaign, 0 otherwise
  • AcceptedCmp5: 1 if customer accepted the offer in the 5th campaign, 0 otherwise
  • Response: 1 if customer accepted the offer in the last campaign, 0 otherwise

Place

  • NumDealsPurchases: Number of purchases made with a discount
  • NumWebPurchases: Number of purchases made through the company’s web site
  • NumCatalogPurchases: Number of purchases made using a catalogue
  • NumStorePurchases: Number of purchases made directly in stores
  • NumWebVisitsMonth: Number of visits to company’s web site in the last month

Acknowledgements¶

O. Parr-Rud. Business Analytics Using SAS Enterprise Guide and SAS Enterprise Miner. SAS Institute, 2014.

Inspiration¶

The main objective is to train a predictive model which allows the company to maximize the profit of the next marketing campaign.

2. Imports Dataset

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In [ ]:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline

import warnings
warnings.filterwarnings('ignore')
In [ ]:
# import dan backup raw dataset
raw_data = pd.read_csv('marketing_campaign.csv', sep=';')
data = raw_data.copy()
data.head()
Out[ ]:
ID Year_Birth Education Marital_Status Income Kidhome Teenhome Dt_Customer Recency MntWines ... NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain Z_CostContact Z_Revenue Response
0 5524 1957 Graduation Single 58138.0 0 0 2012-09-04 58 635 ... 7 0 0 0 0 0 0 3 11 1
1 2174 1954 Graduation Single 46344.0 1 1 2014-03-08 38 11 ... 5 0 0 0 0 0 0 3 11 0
2 4141 1965 Graduation Together 71613.0 0 0 2013-08-21 26 426 ... 4 0 0 0 0 0 0 3 11 0
3 6182 1984 Graduation Together 26646.0 1 0 2014-02-10 26 11 ... 6 0 0 0 0 0 0 3 11 0
4 5324 1981 PhD Married 58293.0 1 0 2014-01-19 94 173 ... 5 0 0 0 0 0 0 3 11 0

5 rows × 29 columns

3. EDA + Business Insights

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3.1 Data Overview

In [ ]:
# menampilkan informasi dataset
data.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2240 entries, 0 to 2239
Data columns (total 29 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   ID                   2240 non-null   int64  
 1   Year_Birth           2240 non-null   int64  
 2   Education            2240 non-null   object 
 3   Marital_Status       2240 non-null   object 
 4   Income               2216 non-null   float64
 5   Kidhome              2240 non-null   int64  
 6   Teenhome             2240 non-null   int64  
 7   Dt_Customer          2240 non-null   object 
 8   Recency              2240 non-null   int64  
 9   MntWines             2240 non-null   int64  
 10  MntFruits            2240 non-null   int64  
 11  MntMeatProducts      2240 non-null   int64  
 12  MntFishProducts      2240 non-null   int64  
 13  MntSweetProducts     2240 non-null   int64  
 14  MntGoldProds         2240 non-null   int64  
 15  NumDealsPurchases    2240 non-null   int64  
 16  NumWebPurchases      2240 non-null   int64  
 17  NumCatalogPurchases  2240 non-null   int64  
 18  NumStorePurchases    2240 non-null   int64  
 19  NumWebVisitsMonth    2240 non-null   int64  
 20  AcceptedCmp3         2240 non-null   int64  
 21  AcceptedCmp4         2240 non-null   int64  
 22  AcceptedCmp5         2240 non-null   int64  
 23  AcceptedCmp1         2240 non-null   int64  
 24  AcceptedCmp2         2240 non-null   int64  
 25  Complain             2240 non-null   int64  
 26  Z_CostContact        2240 non-null   int64  
 27  Z_Revenue            2240 non-null   int64  
 28  Response             2240 non-null   int64  
dtypes: float64(1), int64(25), object(3)
memory usage: 507.6+ KB
In [ ]:
# total unique values setiap feature
data.nunique()
Out[ ]:
ID                     2240
Year_Birth               59
Education                 5
Marital_Status            8
Income                 1974
Kidhome                   3
Teenhome                  3
Dt_Customer             663
Recency                 100
MntWines                776
MntFruits               158
MntMeatProducts         558
MntFishProducts         182
MntSweetProducts        177
MntGoldProds            213
NumDealsPurchases        15
NumWebPurchases          15
NumCatalogPurchases      14
NumStorePurchases        14
NumWebVisitsMonth        16
AcceptedCmp3              2
AcceptedCmp4              2
AcceptedCmp5              2
AcceptedCmp1              2
AcceptedCmp2              2
Complain                  2
Z_CostContact             1
Z_Revenue                 1
Response                  2
dtype: int64
In [ ]:
# Z_CostContact adalah jumlah biaya yang digunakan di setiap kampanye, karena semua nilainya sama di semua pelanggan, maka tidak dapat digunakan dalam analisis
# Z_Revenue adalah jumlah pendapatan yang dihasilkan oleh setiap kampanye, karena semua nilainya sama di semua pelanggan, maka tidak dapat digunakan dalam analisis
# ID adalah CustomerID, karena semua pelanggan adalah unik, maka tidak dapat digunakan dalam analisis
data.drop(['ID', 'Z_CostContact', 'Z_Revenue'], axis=1, inplace=True)
In [ ]:
# identifikasi missing values
pct_missing_values = data.isnull().sum().sum()/data.shape[0]*100
print(f'Total % Missing Values: {pct_missing_values:.2f}%')
Total % Missing Values: 1.07%
In [ ]:
# identifikasi duplicated
pct_duplicates = data[data.duplicated()].shape[0]/data.shape[0]*100
print(f'Total % Duplicated Rows: {pct_duplicates:.2f}%')
Total % Duplicated Rows: 8.12%
In [ ]:
# mengubah tipe data Dt_customer dari object menjadi datetime
data['Dt_Customer'] = pd.to_datetime(data['Dt_Customer'])
In [ ]:
# memisahkan feature berdasarkan tipe data
numeric_cols = []
object_cols = []
datetime_cols = []

# melakukan perulangan setiap feature sesuai dengan tipe data
for col in data.columns:
    if data[col].dtype == 'int64' or data[col].dtype == 'float64':
        numeric_cols.append(col)
    elif data[col].dtype == 'object':
        object_cols.append(col)
    else:
        datetime_cols.append(col)

# print hasilnya yang disimpan pada masing-masing feature
print(f'Total Numeric Columns : {len(numeric_cols)}')
print(f'Total Object Columns  : {len(object_cols)}')
print(f'Total Datetime Columns: {len(datetime_cols)}')
Total Numeric Columns : 23
Total Object Columns  : 2
Total Datetime Columns: 1
In [ ]:
# memisahkan feature berdasarkan klasifikasinya
personal_cols = []
spending_cols = []
purchase_cols = []
campaign_cols = []

# melakukan perulangan setiap feature sesuai dengan klasifikasinya
for col in data.columns:
    if col in 'Year_Birth, Education, Marital_Status, Income, Kidhome, Teenhome, Dt_Customer, Recency, Complain':
        personal_cols.append(col)
    elif col in 'MntWines, MntFruits, MntMeatProducts, MntFishProducts, MntSweetProducts, MntGoldProds':
        spending_cols.append(col)
    elif col in 'NumDealsPurchases, NumWebPurchases, NumCatalogPurchases, NumStorePurchases, NumWebVisitsMonth':
        purchase_cols.append(col)
    elif col in 'AcceptedCmp1, AcceptedCmp2, AcceptedCmp3, AcceptedCmp4, AcceptedCmp5, Response, NumDealsPurchases':
        campaign_cols.append(col)

# mengurutkan hasilnya sesuai dengan alphabet
personal_cols.sort()
spending_cols.sort()
purchase_cols.sort()
campaign_cols.sort()

# print hasilnya yang disimpan pada masing-masing feature
print(f'Personal Columns: {personal_cols}')
print(f'Spending Columns: {spending_cols}')
print(f'Purchase Columns: {purchase_cols}')
print(f'Campaign Columns: {campaign_cols}')
Personal Columns: ['Complain', 'Dt_Customer', 'Education', 'Income', 'Kidhome', 'Marital_Status', 'Recency', 'Teenhome', 'Year_Birth']
Spending Columns: ['MntFishProducts', 'MntFruits', 'MntGoldProds', 'MntMeatProducts', 'MntSweetProducts', 'MntWines']
Purchase Columns: ['NumCatalogPurchases', 'NumDealsPurchases', 'NumStorePurchases', 'NumWebPurchases', 'NumWebVisitsMonth']
Campaign Columns: ['AcceptedCmp1', 'AcceptedCmp2', 'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'Response']

3.2 Statistical Analysis

In [ ]:
# descriptive statistics pada feature bertipe data numerik
data[numeric_cols].describe(percentiles=list(np.linspace(0.1, 0.9,9))).T
Out[ ]:
count mean std min 10% 20% 30% 40% 50% 60% 70% 80% 90% max
Year_Birth 2240.0 1968.805804 11.984069 1893.0 1952.0 1957.0 1962.0 1966.0 1970.0 1973.0 1976.0 1979.0 1984.0 1996.0
Income 2216.0 52247.251354 25173.076661 1730.0 24117.5 32011.0 38198.5 44529.0 51381.5 58482.0 65247.5 71819.0 79844.0 666666.0
Kidhome 2240.0 0.444196 0.538398 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 2.0
Teenhome 2240.0 0.506250 0.544538 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 2.0
Recency 2240.0 49.109375 28.962453 0.0 9.0 19.0 29.0 39.0 49.0 59.0 69.0 79.0 89.0 99.0
MntWines 2240.0 303.935714 336.597393 0.0 6.0 16.0 34.0 81.0 173.5 284.4 418.6 581.2 822.1 1493.0
MntFruits 2240.0 26.302232 39.773434 0.0 0.0 1.0 2.0 4.0 8.0 15.0 25.0 44.0 83.0 199.0
MntMeatProducts 2240.0 166.950000 225.715373 0.0 7.0 12.0 20.0 35.0 67.0 108.4 177.0 298.4 499.0 1725.0
MntFishProducts 2240.0 37.525446 54.628979 0.0 0.0 2.0 3.0 7.0 12.0 20.0 37.0 65.0 120.0 259.0
MntSweetProducts 2240.0 27.062946 41.280498 0.0 0.0 1.0 2.0 5.0 8.0 14.0 26.0 44.2 89.0 263.0
MntGoldProds 2240.0 44.021875 52.167439 0.0 3.0 6.0 11.0 17.0 24.0 34.0 46.0 73.0 122.0 362.0
NumDealsPurchases 2240.0 2.325000 1.932238 0.0 1.0 1.0 1.0 1.0 2.0 2.0 3.0 3.0 5.0 15.0
NumWebPurchases 2240.0 4.084821 2.778714 0.0 1.0 2.0 2.0 3.0 4.0 4.0 5.0 6.0 8.0 27.0
NumCatalogPurchases 2240.0 2.662054 2.923101 0.0 0.0 0.0 1.0 1.0 2.0 2.0 4.0 5.0 7.0 28.0
NumStorePurchases 2240.0 5.790179 3.250958 0.0 2.0 3.0 3.0 4.0 5.0 6.0 7.0 9.0 11.0 13.0
NumWebVisitsMonth 2240.0 5.316518 2.426645 0.0 2.0 3.0 4.0 5.0 6.0 6.0 7.0 7.0 8.0 20.0
AcceptedCmp3 2240.0 0.072768 0.259813 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
AcceptedCmp4 2240.0 0.074554 0.262728 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
AcceptedCmp5 2240.0 0.072768 0.259813 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
AcceptedCmp1 2240.0 0.064286 0.245316 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
AcceptedCmp2 2240.0 0.013393 0.114976 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
Complain 2240.0 0.009375 0.096391 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
Response 2240.0 0.149107 0.356274 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0
In [ ]:
# descriptive statistics pada feature bertipe data object/categorical
data[object_cols].describe().T
Out[ ]:
count unique top freq
Education 2240 5 Graduation 1127
Marital_Status 2240 8 Married 864
In [ ]:
# descriptive statistics pada feature bertipe data datetime
data.describe(include='datetime').T
Out[ ]:
count unique top freq first last
Dt_Customer 2240 663 2012-08-31 12 2012-07-30 2014-06-29

Insight Data Overview & Statistical Analysis¶

  1. Tipe Data Pada Dataset

    • Terdapat 26 Feature bertipe data numerik (integer atau float)
    • Terdapat 3 Feature bertipe data kategori (object)
  2. Missing Values dan Duplicates

    • 1.07% missing values terletak pada Feature Income
    • 8.12% duplicates
  3. Feature dengan Tipe Data Tidak Sesuai:

    • Feature Dt_Customer merupakan tanggal registrasi pelanggan dengan tipe data object. Perlu diubah menjadi tipe Date Time.
  4. Feature dengan Summary Aneh:

    • Feature ID memiliki jumlah nilai unik yang sama dengan jumlah baris dataset (2240), sehingga tidak memungkinkan untuk mengamati riwayat perjalanan pelanggan.
    • Feature Z_CostContact dan Z_Revenue, masing-masing adalah cost dan revenue karena setiap campaign adalah unique maka tidak dapat digunakan dalam analisa.
    • Feature Dt_Customer, pelanggan paling terakhir melakukan registrasi di 29 Juni 2014, maka dengan asumsi saat ini adalah tahun 2014, ada keanehan pada Feature Year_Birth dimana tahun lahir tertua ada di tahun 1893 atau usia pelanggan 121 tahun. Hal ini merupakan hal yang kurang masuk akal. Diduga terdapat kesalahan input tahun lahir oleh pelanggan/kesalahan pencatatan oleh sistem.
    • Feature Income memiliki nilai maksimum mencapai ratusan ribu (666,666), sedangkan nilai ukuran pemusatan dan penyebarannya hanya mencapai puluhan ribu. Diduga nilai ini merupakan outlier yang disebabkan karena kesalahan input atau pencatatan oleh sistem.
    • Beberapa Feature seperti MntFishProducts, MntFruits, MntGoldProds, MntMeatProducts, MntSweetProducts, MntWines memiliki nilai maksimum yang jauh dari ukuran pemusatan atau penyebaran lainnya, menunjukkan adanya outlier, namun hal ini perlu dipertimbangkan kembali dari konteks bisnis apakah wajar atau tidaknya pembelian dalam jumlah tersebut.
    • Feature Marital Status memiliki 8 nilai unik dan Feature Education memiliki 5 nilai unik.
    • Terdapat invalid values pada Feature Marital_Status seperti YOLO, Alone, dan Absurd. Dan pada Feature Education yaitu Basic dan 2n Cycle, apakah termasuk dalam level pendidikan formal atau bukan.

3.3 Univariate Analysis

3.3.1 Distribution of Numerical Feature

In [ ]:
plt.figure(figsize=(20, 10))
for i in range(0, len(numeric_cols)):
    plt.subplot(5, 5, i + 1)
    sns.kdeplot(data[numeric_cols[i]])
    plt.tight_layout()

plt.show()

Insight Distribusi Data:¶

  • Normal Distribution:
    • Recency memiliki distribusi yang mirip dengan distribusi normal.
  • Left-Skewed Distribution:
    • Year_Birth menunjukkan kecondongan ke kiri dengan median yang lebih tinggi daripada mean.
  • Right-Skewed Distribution:
    • Income dan beberapa features terkait pembelian produk menunjukkan kecondongan ke kanan, dengan mean yang lebih tinggi daripada median.
  • Bimodal Distribution:
    • Kidhome dan Teenhome menunjukkan dua puncak dalam distribusinya.
  • Binary Dominated Distribution:
    • Beberapa features campaign (AcceptedCmp1, AcceptedCmp2, AcceptedCmp3, AcceptedCmp4, AcceptedCmp5, dan Responsee) didominasi oleh nilai 0.
In [ ]:
plt.figure(figsize=(20, 10))
for i in range(0, len(numeric_cols[:-7])):
    plt.subplot(4, 4, i+1)
    sns.boxplot(y=data[numeric_cols[i]])
    plt.tight_layout()

Insight Box Plot:¶

  • Year_Birth dan Income memiliki outlier seperti yang telah dijelaskan pada bagian sebelumnya, yakni pada angka 666.666 (Income) dan 1893 (Year_Birth).
  • Beberapa features terkait pembelian produk dan aktivitas pembelian memiliki banyak nilai outlier pada nilai tinggi, menunjukkan variasi yang signifikan dalam pola pembelian atau aktivitas pelanggan.

3.3.2 Distribution of Categorical Feature

In [ ]:
# setup ukuran figure
plt.figure(figsize=(12, 8))

# iterasi untuk setiap Feature objek
for i in range(0, len(object_cols)):
    # mendapatkan urutan kategori berdasarkan jumlah masing-masing
    order = raw_data[object_cols[i]].value_counts().index

    # membuat subplot
    plt.subplot(2, len(object_cols), i + 1)

    # countplot dengan urutan kategori yang diinginkan
    ax = sns.countplot(x=raw_data[object_cols[i]], order=order, palette=sns.color_palette())

    # menambahkan label di atas setiap bar
    for p in ax.patches:
        ax.annotate(f'{p.get_height()}', (p.get_x() + p.get_width() / 2., p.get_height()),
                    ha='center', va='center', xytext=(0, 5), textcoords='offset points')

    # setup pada xticks
    plt.xticks(rotation=0, ha='center')

plt.tight_layout()
plt.show()

Insight Countplot/Barplot (Categorical Feature):¶

  • Level Pendidikan:
    • Level Pendidikan: Mayoritas pelanggan memiliki tingkat pendidikan "Graduation," menunjukkan target pasar utama.
  • Status Perkawinan:
    • Mayoritas pelanggan memiliki status perkawinan "Married," menunjukkan dominasi pasangan dalam populasi pelanggan.
  • Pertimbangan Invalid Data:
    • Seperti dibahas pada bagian sebelumnya adanya data yang tidak valid pada Feature Marital_Status dan Education.
In [ ]:
# exclude data yang tidak valid seperti Basic dan 2n Cycle pada Education dan Absurd, Alone, YOLO pada Marital Status
filter_data = (data['Education'].isin(['Graduation', 'Master', 'PhD'])) \
    & (data['Marital_Status'].isin(['Single', 'Married', 'Divorced']))
In [ ]:
# 'Education'
education_counts = data[filter_data].groupby(
    'Education')['Response'].value_counts(normalize=True).unstack()
colors = ['#e41a1c', '#377eb8']

fig, axes = plt.subplots(1, 2, figsize=(12, 6))

education_counts.plot(kind='bar', stacked=True, color=colors, ax=axes[0])
axes[0].set_title('Percentage of Response by Education')
axes[0].set_ylabel('Percentage')
axes[0].set_xticklabels(education_counts.index, rotation=0, ha='center')

handles, labels = axes[0].get_legend_handles_labels()
axes[0].legend(reversed(handles), reversed(labels), bbox_to_anchor=(
    1.2, 1), loc='upper right', title='Response')

for p in axes[0].patches:
    if p.get_height() >= 0.5:
        vertical_position = 'bottom' if p.get_height() < 0.5 else 'top'
        axes[0].annotate(f'{p.get_height():.0%}', (p.get_x() + p.get_width() / 2., p.get_height()),
                         ha='center', va=vertical_position, xytext=(0, 10 if p.get_height() < 0.5 else -10),
                         fontsize=12, color='white',
                         textcoords='offset points')
for p in axes[0].patches:
    if p.get_height() < 0.5:
        axes[0].annotate(f'{p.get_height():.0%}', (p.get_x() + p.get_width() / 2., 1),
                         ha='center', va='top', xytext=(0, -1),
                         fontsize=12, color='white',
                         textcoords='offset points')

# 'Marital_Status'
marital_counts = data[filter_data].groupby(
    'Marital_Status')['Response'].value_counts(normalize=True).unstack()
marital_counts.plot(kind='bar', stacked=True, color=colors, ax=axes[1])
axes[1].set_title('Percentage of Response by Marital_Status')
axes[1].set_ylabel('Percentage')
axes[1].set_xticklabels(marital_counts.index, rotation=0, ha='center')

handles, labels = axes[1].get_legend_handles_labels()
axes[1].legend(reversed(handles), reversed(labels), bbox_to_anchor=(
    1.2, 1), loc='upper right', title='Response')

for p in axes[1].patches:
    if p.get_height() >= 0.5:
        vertical_position = 'bottom' if p.get_height() < 0.5 else 'top'
        axes[1].annotate(f'{p.get_height():.0%}', (p.get_x() + p.get_width() / 2., p.get_height()),
                         ha='center', va=vertical_position, xytext=(0, 10 if p.get_height() < 0.5 else -10),
                         fontsize=12, color='white',
                         textcoords='offset points')
for p in axes[1].patches:
    if p.get_height() <= 0.5:
        axes[1].annotate(f'{p.get_height():.0%}', (p.get_x() + p.get_width() / 2., 1),
                         ha='center', va='top', xytext=(0, -1),
                         fontsize=12, color='white',
                         textcoords='offset points')

plt.tight_layout()
plt.show()

Insight Percentage of Response (Categorical Feature vs Target):¶

  • Tingkat Pendidikan:

    • Tingkat Response tertintinggi pada Level Pendidikan: PhD (21%), Master (15%), dan Graduation (13%).
    • Persentase response pada Level Pendidikan berbanding lurus, semakin tinggi level pendidikan maka semakin tinggi persentase
  • Status Perkawinan:

    • Tingkat Response tertinggi pada Status Perkawinan: Single (22%), Divorced (21%), dan Married (11%).
    • Persentase response pada Status Perkawinan berbanding terbalik, dimana yang berpasangan (semakin tinggi jumlah anggota keluarga) maka semakin rendah persentasenya.

3.2.3 Distribution of Datetime Feature

In [ ]:
# membuat function untuk menampilkan trend distribusi pelanggan yang melakukan registrasi/enrollment setiap bulannya
def monthly_distribution(data, date_col, figsize=(12, 5)):
    # setup subplot untuk line plot
    fig, ax = plt.subplots(figsize=figsize)

    # resample data dan hitung jumlah bulanan
    monthly_counts = data.resample('M', on=date_col).size()

    # plot line distribusi datetime (per bulan)
    ax.plot(monthly_counts.index, monthly_counts.values,
            marker='o', linestyle='-')
    ax.set_title(f'Customer Registration Distribution Trends')
    ax.set_xlabel(date_col)
    ax.set_ylabel('frekuensi')

    # memutar label pada sumbu x agar lebih mudah dibaca
    plt.xticks(rotation=45)

    # membuat indeks bulanan dari Juli 2012 hingga Juni 2014
    monthly_index = pd.date_range(
        start='2012-07-01', end='2014-06-30', freq='M')

    # melakukan reindex dengan nilai pada variabel monthly_index
    monthly_counts = monthly_counts.reindex(monthly_index, fill_value=0)

    # mengubah format xticklabels menjadi nama bulan-tahun
    month_year_labels = [date.strftime('%b-%Y')
                         for date in monthly_counts.index]
    ax.set_xticks(monthly_counts.index)
    ax.set_xticklabels(month_year_labels)

    # menampilkan plot
    plt.tight_layout()
    plt.show()
In [ ]:
# memanggil function menampilkan trend pelanggan yang registrasi/enrollment
monthly_distribution(data, 'Dt_Customer')

Insight (Datetime Feature):¶

  • Terdapat fluktuasi dalam pendaftaran pelanggan selama periode Juli-2012 hingga Juni-2014.
  • Puncak pendaftaran terjadi pada bulan Agustus-2012 dan Oktober-2013.
  • Tren menunjukkan jumlah pendaftaran yang lebih rendah pada awal dan akhir periode, menunjukkan potensi pola musiman atau faktor lain yang memengaruhi pendaftaran pelanggan.
In [ ]:
# membuat function untuk menampilkan trend distribusi pelanggan yang melakukan registrasi/enrollment setiap bulannya bersadarkan response
def monthly_distribution_response(data, date_col, figsize=(12, 5)):

    # setup subplot untuk line plot
    fig, ax = plt.subplots(figsize=figsize)

    # resample data dan hitung jumlah bulanan untuk response = 0
    monthly_counts_0 = data[data['Response'] == 0].resample('M', on='Dt_Customer').size()

    # plot line distribusi datetime (per bulan) untuk response = 0
    ax.plot(monthly_counts_0.index, monthly_counts_0.values,
            marker='o', linestyle='-', label='Response = 0', color = "#ff0000")

    # resample data dan hitung jumlah bulanan untuk response = 1
    monthly_counts_1 = data[data['Response'] == 1].resample('M', on='Dt_Customer').size()

    # plot line distribusi datetime (per bulan) untuk response = 1
    ax.plot(monthly_counts_1.index, monthly_counts_1.values,
            marker='o', linestyle='-', label='Response = 1', color = "#438cc4")

    # Set title, xlabel, dan ylabel
    ax.set_title('Customer Registration Distribution Trends')
    ax.set_xlabel('Dt_Customer')
    ax.set_ylabel('Frekuensi')

    # memutar label pada sumbu x agar lebih mudah dibaca
    plt.xticks(rotation=45)

    # membuat indeks bulanan dari Juli 2012 hingga Juni 2014
    monthly_index = pd.date_range(
        start='2012-07-01', end='2014-06-30', freq='M')

    # melakukan reindex dengan nilai pada variabel monthly_index untuk response = 0
    monthly_counts_0 = monthly_counts_0.reindex(monthly_index, fill_value=0)

    # melakukan reindex dengan nilai pada variabel monthly_index untuk response = 1
    monthly_counts_1 = monthly_counts_1.reindex(monthly_index, fill_value=0)

    # mengubah format xticklabels menjadi nama bulan-tahun
    month_year_labels = [date.strftime('%b-%Y')
                         for date in monthly_index]
    ax.set_xticks(monthly_index)
    ax.set_xticklabels(month_year_labels)

    # Menambah legenda
    ax.legend()

    # menampilkan plot
    plt.tight_layout()
    plt.show()
In [ ]:
# memanggil function menampilkan trend pelanggan yang registrasi/enrollment berdasarkan response
monthly_distribution_response(data, 'Dt_Customer')

Insight (Datetime Feature vs Target):¶

  • Pada rentang waktu Juli 2012 hingga Juni 2014, lebih banyak pelanggan yang mendaftar namun tidak merespon campaign.
  • Puncak pendaftaran dan respon terjadi pada Agustus 2012 - September 2012, namun jumlahnya cenderung menurun hingga akhir periode.
  • Analisis ini memberikan gambaran tentang dinamika pendaftaran dan respon campaign selama periode tersebut, yang dapat menjadi dasar untuk strategi pemasaran yang lebih efektif di masa depan.

3.4 Multivariate Analysis

3.4.1 Based on Regression Line

In [ ]:
# regplot setiap feature ke target
plt.figure(figsize=(10, 20))
plt.subplots_adjust(hspace=0.5, wspace=0.5)

for i, col in enumerate(data[numeric_cols[1:-1]]):
    plt.subplot(7, 3, i+1)
    sns.regplot(data=data, x=data[col], y=data['Response'], logistic=True)
    plt.title(f'{col} vs Response')
    plt.xlabel(f'{col}')
    plt.ylabel('Response')

plt.tight_layout()
plt.show()

Insight Regression Plot (Numerical Feature vs Target):¶

  • Pengaruh pendapatan (Income) mempunyai korelasi positif yang cukup kuat dimana semakin tinggi income maka probabilitas merespon campaign semakin tinggi. Contohnya saat Income berada pada angka 200000 mempunyai probabilitas response sekitar 60%.
  • Sebaliknya pengaruh mempunyai anak (Teenhome, Kidhome) mempunyai korelasi negatif dimana jumlah anak akan menurunkan probabilitas meresponse campaign. Contohnya saat mempunyai 2 anak remaja (Teenhome) akan menurunkan probabilitas campaign sampai mendekati 0%, sedangkan saat tidak mempunyai anak remaja probabilitas mencapai 20%.
  • Spending pada produk tertentu, terutama MeatProducts, juga memiliki korelasi positif yang signifikan dengan Response. Semakin banyak produk yang dibeli maka probabilitas untuk meresponse campaign semakin tinggi.
  • Jenis pembelian melalui Katalog dan Web memberikan kontribusi positif yang signifikan terhadap kemungkinan pelanggan merespon, sementara pembelian dengan diskon atau melalui toko fisik tidak menunjukkan korelasi yang signifikan.
  • Campaign (AcceptedCmp1, AcceptedCmp2, AcceptedCmp3, AcceptedCmp4, AcceptedCmp5) juga memiliki korelasi positif yang signifikan terhadap Response.

3.4.2 Based on Correlation Coefficient (Heatmap)

In [ ]:
# menghitung korelasi dengan Response
CORR_RESPONSE = data.corrwith(data['Response'], axis=0, method='pearson', drop=False).sort_values(ascending=False)

# setup figure dan color palette
cmap = sns.diverging_palette(10, 240, as_cmap=True)
plt.figure(figsize=(6, 6))

# heatmap response
sns.heatmap(CORR_RESPONSE.to_frame(), annot=True, fmt=".2f", cbar=True, center=0, cmap=cmap)
plt.title('Correlation to Response')
Out[ ]:
Text(0.5, 1.0, 'Correlation to Response')
In [ ]:
# setup figure dan color palette
cmap = sns.diverging_palette(10, 240, as_cmap=True)
plt.figure(figsize=(12, 8))

# heatmap matrix correlation
sns.heatmap(data.corr(), annot=True, fmt=".2f", cbar=True, center=0, cmap=cmap)
plt.title('Matrix Correlation')

plt.tight_layout()
plt.show()

Insight Correlation of Coefficient (Heatmap):¶

  • Pentingnya Korelasi Antara Feature:
    • Korelasi Positif terhadap Target pada campaign, produk spending, dan channel penjualan.
    • Korelasi Negatif terhadap Target pada Recency dan jumlah anak (Teenhome, Kidhome).
    • Korelasi Tinggi Antara Beberapa Pasang Feature, menunjukkan adanya multicollinearity.
    • Rekomendasi untuk Proses Modeling.

3.5 Business Insights

In [ ]:
# menghitung jumlah responden dengan Yes Response tanpa menerima campaign manapun
no_campaign_response = len(data[(data['AcceptedCmp1'] == 0) & (data['AcceptedCmp2'] == 0) & (
    data['AcceptedCmp3'] == 0) & (data['AcceptedCmp4'] == 0) & (data['AcceptedCmp5'] == 0) & (data['Response'] == 1)])

# menghitung jumlah responden dengan Yes Response
total_yes_response = len(data[data['Response'] == 1])

# menghitung jumlah responden dengan Yes Response yang setidaknya menerima satu campaign
at_least_one_campaign_response = total_yes_response - no_campaign_response

# menampilkan hasil
print('Total Yes Response:', total_yes_response)
print('Yes Response without accepting any campaigns:', no_campaign_response)
print('Yes Response accepting at least one campaign:',
      at_least_one_campaign_response)

# menampilkan proporsi dalam persentase
percentage_no_campaign_response = (
    no_campaign_response / total_yes_response) * 100
percentage_at_least_one_campaign_response = (
    at_least_one_campaign_response / total_yes_response) * 100

print('Percentage of Yes Response without accepting any campaigns: {:.1f}%'.format(
    percentage_no_campaign_response))
print('Percentage of Yes Response accepting at least one campaign: {:.1f}%'.format(
    percentage_at_least_one_campaign_response))
Total Yes Response: 334
Yes Response without accepting any campaigns: 146
Yes Response accepting at least one campaign: 188
Percentage of Yes Response without accepting any campaigns: 43.7%
Percentage of Yes Response accepting at least one campaign: 56.3%
In [ ]:
# success rate untuk setiap campaign yang diberikan
# inisialisasi dataFrame
campaigns = ['Cmp1', 'Cmp2', 'Cmp3', 'Cmp4', 'Cmp5']
df_marketing = pd.DataFrame(
    0, index=campaigns + ['Any campaigns', 'Total Response'], columns=['Count', 'Yes Response', 'Success Rate'])

# menghitung metrik untuk setiap campaign
for col in ['AcceptedCmp1', 'AcceptedCmp2', 'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5']:
    count = data[col].sum()
    yes_response = data[(data[col] == 1) & (data['Response'] == 1)].shape[0]
    success_rate = np.round(yes_response / count * 100, 2)

    # mengisi nilai DataFrame
    df_marketing.loc[col[8:], ['Count', 'Yes Response', 'Success Rate']] = [
        count, yes_response, success_rate]

# menghitung metrik untuk 'Any campaigns' dan 'Total Response'
df_marketing.loc['Any campaigns', 'Count'] = len(data) - 1777
df_marketing.loc['Any campaigns', 'Yes Response'] = len(
    data[data['Response'] == 1])
df_marketing.loc['Any campaigns', 'Success Rate'] = np.round(
    df_marketing.loc['Any campaigns', 'Yes Response'] / df_marketing.loc['Any campaigns', 'Count'] * 100, 2)

df_marketing.loc['Total Response', 'Count'] = len(data)
df_marketing.loc['Total Response', 'Yes Response'] = len(
    data[data['Response'] == 1])
df_marketing.loc['Total Response', 'Success Rate'] = np.round(
    df_marketing.loc['Total Response', 'Yes Response'] / len(data) * 100, 2)

# menampilkan dataFrame
df_marketing
Out[ ]:
Count Yes Response Success Rate
Cmp1 144 79 54.86
Cmp2 30 20 66.67
Cmp3 163 77 47.24
Cmp4 167 62 37.13
Cmp5 163 92 56.44
Any campaigns 463 334 72.14
Total Response 2240 334 14.91
In [ ]:
# subplots
fig, axes = plt.subplots(2, 1, figsize=(7, 7))
ax = axes.ravel()

# pie chart
colors = ['#377eb8','#e41a1c']
labels = ['Accepting at least one campaign', 'Not accepting any campaign']
sizes = [percentage_at_least_one_campaign_response,
         percentage_no_campaign_response]
ax[0].pie(sizes, labels=labels, autopct='%1.1f%%', colors=colors, startangle=90, textprops={'color': 'white'})
ax[0].set_title('Percentage of Yes Response with and without Accepting Campaigns')

# bar chart for Success Rate
ax[1].bar(df_marketing.index[:-2], df_marketing['Success Rate'][:-2], color='#377eb8', alpha=0.7, label='Success Rate')
ax[1].set_xlabel('Campaigns')
ax[1].set_ylabel('Success Rate (%)')
ax[1].set_title('Success Rate for Each Campaign')

# Menghilangkan garis x dan y
ax[1].spines['top'].set_visible(False)
ax[1].spines['right'].set_visible(False)
ax[1].spines['bottom'].set_visible(True)
ax[1].spines['left'].set_visible(True)

ax[1].tick_params(axis='both', which='both', length=0)  # Menghilangkan tanda tick pada x dan y

# values pada bar chart
for i, v in enumerate(df_marketing['Success Rate'][:-2]):
    ax[1].text(i, v + 1, str(v) + '%', ha='center', va='bottom', fontsize=8)

plt.tight_layout(h_pad=1)
plt.show()
In [ ]:
# membuat function untuk menampilkan plot pada agregasi data
def plot_grouped(data, group_column, variables, nrows=1, ncols=1, method='mean', hue=None):

    # melakukan filter pada feature yang bertipe data numerik
    numeric_variables = [
        var for var in variables if pd.api.types.is_numeric_dtype(data[var])]

    # agregasi data berdasarkan hasil feature yang telah terfilter sebelumnya
    grouped_data = data.groupby([group_column, hue]).agg(
        {variable: method for variable in numeric_variables}).reset_index()

    # setup color palette
    palette = sns.color_palette('Set1', len(numeric_variables))

    # setup subplots
    fig, axes = plt.subplots(nrows=nrows, ncols=ncols,
                             figsize=(10 * ncols, 5 * nrows))

    # untuk memastikan bahwa axes tetap iterable untuk kasus 1 subplot
    if nrows == 1 and ncols == 1:
        axes = [axes]

    # agregasi sesuai dengan methode yang terdefinisi pada parameter
    if method == 'mean':
        title = 'Average'
    elif method == 'sum':
        title = 'Total'
    elif method == 'count':
        title = 'Count'

    # melakukan perulangan pada setiap feature yang ditampilkan
    for i, variable in enumerate(numeric_variables):

        # menentukan posisi axes setiap feature
        row_index = i // ncols
        col_index = i % ncols
        ax = sns.barplot(x=group_column, y=variable, hue=hue,
                         data=grouped_data, ax=axes[row_index][col_index], palette=palette)

        # menentukan title, xlabel, ylabel, dan xticks
        axes[row_index][col_index].set_title(
            f'{title} {variable} by {group_column}')
        axes[row_index][col_index].set_ylabel(variable)
        axes[row_index][col_index].set_xlabel(group_column)
        axes[row_index][col_index].tick_params(axis='x', rotation=0)

        # menambahkan label angka di atas setiap bar
        for p in ax.patches:
            height = p.get_height()
            axes[row_index][col_index].text(p.get_x() + p.get_width()/2., height,
                                            f'{height:.2f}', ha='center', va='bottom', color='black', fontsize=10)

    plt.tight_layout()
    plt.show()
In [ ]:
# memilih feature yang akan ditampilkan insightnya
cols_to_plot = ['Income', 'Recency',
                'MntMeatProducts', 'MntWines']

# memanggil function untuk menampilkan barplot pada agregasi feature education
plot_grouped(data[filter_data], 'Education', cols_to_plot,
             nrows=2, ncols=2, hue='Response')
In [ ]:
# memanggil function untuk menampilkan barplot pada agregasi marital status
plot_grouped(data[filter_data], 'Marital_Status', cols_to_plot,
             nrows=2, ncols=2, hue='Response')

Summary Business Insights¶

Berdasarkan analisis mendalam terhadap data campaign dan karakteristik pelanggan, kami menyarankan beberapa langkah strategis untuk meningkatkan efektivitas campaign dan memaksimalkan keuntungan bisnis:

  1. Segmentasi Pelanggan Berdasarkan Respons campaign:

    Melakukan segmentasi pelanggan berdasarkan respons campaign dapat membantu dalam menyesuaikan strategi pemasaran. Fokuskan upaya pada kelompok pelanggan yang telah menunjukkan respons positif, seperti tingkat pendidikan Graduation, PhD, dan Master, serta status pernikahan Single, Married, dan Divorced.

  2. Personalisasi Pesan dan Penawaran:

    Personalisasi pesan dan penawaran campaign untuk setiap kelompok pelanggan yang telah diidentifikasi dapat meningkatkan keterlibatan. Berdasarkan karakteristik unik dari setiap kelompok, buatlah pesan yang relevan dan tawarkan insentif yang sesuai dengan preferensi mereka.

  3. Penargetan Tingkat Pendidikan Tinggi:

    Tingkat pendidikan tinggi seperti Graduation, PhD, dan Master memiliki potensi besar untuk respons campaign. Fokuskan penawaran khusus, informasi produk, dan keuntungan tambahan pada kelompok ini untuk memaksimalkan partisipasi.

  4. Optimalkan Pengeluaran Pelanggan yang Merespon:

    Pelanggan yang merespons campaign memiliki kecenderungan pengeluaran yang lebih tinggi pada berbagai kategori produk. Optimalisasi persediaan dan promosi pada produk-produk yang paling diminati oleh kelompok pelanggan ini dapat meningkatkan nilai transaksi.

  5. Perkuat campaign dengan Data Pembelian dan Channel:

    Analisis menunjukkan bahwa pelanggan yang merespons campaign memiliki rata-rata pembelian yang lebih tinggi di berbagai saluran seperti catalog, web, dan toko fisik. Penguatan campaign dengan peningkatan ketersediaan produk melalui saluran ini dapat meningkatkan aksesibilitas produk bagi pelanggan.

  6. Pemantauan Terus-Menerus dan Analisis Reaksi Pelanggan:

    Melakukan pemantauan terus-menerus terhadap respons pelanggan dan melakukan analisis lebih lanjut terhadap perubahan tren dan preferensi. Keterlibatan yang berkelanjutan dan penyesuaian cepat terhadap dinamika pasar dapat menjadi kunci kesuksesan jangka panjang.

Dengan menerapkan strategi ini, diharapkan perusahaan dapat meraih keberhasilan yang lebih besar dalam campaign pemasaran, meningkatkan loyalitas pelanggan, dan mengoptimalkan hasil bisnis secara keseluruhan.

4. Data Pre-Processing

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4.1 Data Split

In [ ]:
# melakukan backup dataset sebelum split
data_before_splitting = data.copy()
In [ ]:
# restore point
data = data_before_splitting.copy()
data.head()
Out[ ]:
Year_Birth Education Marital_Status Income Kidhome Teenhome Dt_Customer Recency MntWines MntFruits ... NumCatalogPurchases NumStorePurchases NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain Response
0 1957 Graduation Single 58138.0 0 0 2012-09-04 58 635 88 ... 10 4 7 0 0 0 0 0 0 1
1 1954 Graduation Single 46344.0 1 1 2014-03-08 38 11 1 ... 1 2 5 0 0 0 0 0 0 0
2 1965 Graduation Together 71613.0 0 0 2013-08-21 26 426 49 ... 2 10 4 0 0 0 0 0 0 0
3 1984 Graduation Together 26646.0 1 0 2014-02-10 26 11 4 ... 0 4 6 0 0 0 0 0 0 0
4 1981 PhD Married 58293.0 1 0 2014-01-19 94 173 43 ... 3 6 5 0 0 0 0 0 0 0

5 rows × 26 columns

In [ ]:
# split train and test set

# memisahkan antara training dan test set
from sklearn.model_selection import train_test_split
data_train, data_test = train_test_split(data, test_size=0.2, random_state=42)

# menampilkan shape dari train dan test set
print(f'data_train: {data_train.shape}, data_test: {data_test.shape}')
data_train: (1792, 26), data_test: (448, 26)

4.2 Handling Missing Values

In [ ]:
# backup dataset sebelum handling missing values
data_before_handling_missingvalues_train = data_train.copy()
data_before_handling_missingvalues_test = data_test.copy()
In [ ]:
# restore point data_train
data_train = data_before_handling_missingvalues_train.copy()
data.head()
Out[ ]:
Year_Birth Education Marital_Status Income Kidhome Teenhome Dt_Customer Recency MntWines MntFruits ... NumCatalogPurchases NumStorePurchases NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain Response
0 1957 Graduation Single 58138.0 0 0 2012-09-04 58 635 88 ... 10 4 7 0 0 0 0 0 0 1
1 1954 Graduation Single 46344.0 1 1 2014-03-08 38 11 1 ... 1 2 5 0 0 0 0 0 0 0
2 1965 Graduation Together 71613.0 0 0 2013-08-21 26 426 49 ... 2 10 4 0 0 0 0 0 0 0
3 1984 Graduation Together 26646.0 1 0 2014-02-10 26 11 4 ... 0 4 6 0 0 0 0 0 0 0
4 1981 PhD Married 58293.0 1 0 2014-01-19 94 173 43 ... 3 6 5 0 0 0 0 0 0 0

5 rows × 26 columns

In [ ]:
# restore point data_test
data_test = data_before_handling_missingvalues_test.copy()
data.head()
Out[ ]:
Year_Birth Education Marital_Status Income Kidhome Teenhome Dt_Customer Recency MntWines MntFruits ... NumCatalogPurchases NumStorePurchases NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain Response
0 1957 Graduation Single 58138.0 0 0 2012-09-04 58 635 88 ... 10 4 7 0 0 0 0 0 0 1
1 1954 Graduation Single 46344.0 1 1 2014-03-08 38 11 1 ... 1 2 5 0 0 0 0 0 0 0
2 1965 Graduation Together 71613.0 0 0 2013-08-21 26 426 49 ... 2 10 4 0 0 0 0 0 0 0
3 1984 Graduation Together 26646.0 1 0 2014-02-10 26 11 4 ... 0 4 6 0 0 0 0 0 0 0
4 1981 PhD Married 58293.0 1 0 2014-01-19 94 173 43 ... 3 6 5 0 0 0 0 0 0 0

5 rows × 26 columns

In [ ]:
# membuat function untuk mengidentifikasi missing values
def identify_missing_values(data):

    # menghitung missing values pada setiap feature
    missing_values_count = data.isnull().sum().reset_index().rename(
        {'index': 'column', 0: 'missing values'}, axis=1)

    # filter feature yang mempunyai missing values
    missing_values_count = missing_values_count[missing_values_count['missing values'] > 0]

    # menghitng persentase missing values
    missing_values_count['percentage'] = round(
        (missing_values_count['missing values'] / len(data) * 100), 2)

    # mengurutkan jumlah missing values secara descending
    missing_values_count = missing_values_count.sort_values(
        by='missing values', ascending=False).reset_index(drop=True)

    return missing_values_count
In [ ]:
# menampilkan data shape dan memanggil function untuk mengidentifikasi missing values
print(f'Data Train Shape : {data_train.shape}')
print(f'Data Test Shape : {data_test.shape}')
print(identify_missing_values(data_train))
print(identify_missing_values(data_test))
Data Train Shape : (1792, 26)
Data Test Shape : (448, 26)
   column  missing values  percentage
0  Income              19        1.06
   column  missing values  percentage
0  Income               5        1.12
In [ ]:
# handling missing values, dengan menghapusnya (<10%)
data_train.dropna(axis=0, inplace=True)
data_test.dropna(axis=0, inplace=True)
print(data_train.shape)
print(data_test.shape)
(1773, 26)
(443, 26)

4.3 Handling Duplicates Values

In [ ]:
# melakukan backup dataset sebelum handling duplicates
data_before_handling_duplicates_train = data_train.copy()
data_before_handling_duplicates_test = data_test.copy()
In [ ]:
# restore point
data_train = data_before_handling_duplicates_train.copy()
data_test = data_before_handling_duplicates_test.copy()
In [ ]:
# membuat function untuk mengidentifikasi data terduplikasi
def identify_duplicates(df):

    # mencari baris yang terduplikasi
    duplicate_rows = df[df.duplicated()]

    # menghitng jumlah row yang terduplikasi
    duplicate_count = len(duplicate_rows)

    # membuat dataframe untuk menampilkan jumlah row beserta persentase data yang terduplikasi
    if duplicate_count > 0:
        duplicate_info = pd.DataFrame({
            'duplicated rows': [duplicate_count],
            'percentage': round((duplicate_count / len(df) * 100), 2)
        })
    else:
        duplicate_info = pd.DataFrame({
            'duplicated rows': [0],
            'percentage': [0.0]
        })

    return duplicate_info
In [ ]:
# menampikan data shape dan memanggil function untuk mengidentikasi data yang terduplikasi
print(f'Data Train Shape : {data_train.shape}')
print(f'Data Test Shape : {data_test.shape}')
print(identify_duplicates(data_train))
print(identify_duplicates(data_test))
Data Train Shape : (1773, 26)
Data Test Shape : (443, 26)
   duplicated rows  percentage
0              116        6.54
   duplicated rows  percentage
0                6        1.35
In [ ]:
# menampilkan baris data yang terduplikasi pada data_train
data_train[data_train.duplicated(keep=False)].sort_values(by=['Education','Marital_Status','Income'])
Out[ ]:
Year_Birth Education Marital_Status Income Kidhome Teenhome Dt_Customer Recency MntWines MntFruits ... NumCatalogPurchases NumStorePurchases NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain Response
1107 1970 2n Cycle Married 15315.0 0 0 2013-08-03 27 7 4 ... 0 4 5 0 0 0 0 0 0 0
558 1970 2n Cycle Married 15315.0 0 0 2013-08-03 27 7 4 ... 0 4 5 0 0 0 0 0 0 0
1549 1975 2n Cycle Married 37284.0 1 1 2013-03-29 46 11 1 ... 0 3 6 0 0 0 0 0 0 0
2015 1975 2n Cycle Married 37284.0 1 1 2013-03-29 46 11 1 ... 0 3 6 0 0 0 0 0 0 0
669 1971 2n Cycle Married 54690.0 1 1 2013-11-07 76 111 16 ... 1 5 3 0 0 0 0 0 0 0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2062 1982 PhD Together 70038.0 0 0 2013-10-25 54 587 54 ... 4 8 2 0 0 0 0 0 0 0
1630 1947 PhD Together 73059.0 0 1 2013-08-31 36 410 112 ... 3 13 4 0 0 0 0 0 0 0
658 1947 PhD Together 73059.0 0 1 2013-08-31 36 410 112 ... 3 13 4 0 0 0 0 0 0 0
1649 1950 PhD Widow 56551.0 1 1 2014-05-07 48 67 4 ... 1 4 4 0 0 0 0 0 0 0
1959 1950 PhD Widow 56551.0 1 1 2014-05-07 48 67 4 ... 1 4 4 0 0 0 0 0 0 0

226 rows × 26 columns

In [ ]:
# menampilkan baris data yang terduplikasi pada data_test
data_test[data_test.duplicated(keep=False)].sort_values(by=['Education','Marital_Status','Income'])
Out[ ]:
Year_Birth Education Marital_Status Income Kidhome Teenhome Dt_Customer Recency MntWines MntFruits ... NumCatalogPurchases NumStorePurchases NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain Response
508 1992 Graduation Married 34935.0 0 0 2013-06-21 71 34 4 ... 1 4 7 0 0 0 0 0 0 0
1578 1992 Graduation Married 34935.0 0 0 2013-06-21 71 34 4 ... 1 4 7 0 0 0 0 0 0 0
1318 1972 Graduation Married 40321.0 1 1 2013-07-29 59 44 4 ... 0 3 7 0 0 0 0 0 0 0
316 1972 Graduation Married 40321.0 1 1 2013-07-29 59 44 4 ... 0 3 7 0 0 0 0 0 0 0
705 1986 Graduation Married 71952.0 1 0 2013-01-10 93 656 80 ... 4 8 4 1 0 1 0 0 0 0
351 1986 Graduation Married 71952.0 1 0 2013-01-10 93 656 80 ... 4 8 4 1 0 1 0 0 0 0
1822 1971 Master Single 33316.0 1 1 2013-10-04 34 79 1 ... 1 4 6 0 0 0 0 0 0 0
1359 1971 Master Single 33316.0 1 1 2013-10-04 34 79 1 ... 1 4 6 0 0 0 0 0 0 0
1545 1986 Master Together 42386.0 1 0 2013-01-13 43 65 4 ... 0 3 8 0 0 0 0 0 0 0
297 1986 Master Together 42386.0 1 0 2013-01-13 43 65 4 ... 0 3 8 0 0 0 0 0 0 0
1347 1983 PhD Married 50150.0 0 0 2013-06-20 32 135 46 ... 2 7 5 0 0 0 0 0 0 0
99 1983 PhD Married 50150.0 0 0 2013-06-20 32 135 46 ... 2 7 5 0 0 0 0 0 0 0

12 rows × 26 columns

In [ ]:
# handling data yang terduplikasi dengan cara menghapusnya
data_train.drop_duplicates(keep='first', inplace=True)
data_test.drop_duplicates(keep='first', inplace=True)
print(f'Data Train Shape : {data_train.shape}')
print(f'Data Test Shape : {data_test.shape}')
Data Train Shape : (1657, 26)
Data Test Shape : (437, 26)

4.4 Handling Outliers

In [ ]:
# melakukan backup dataset sebelum handling outliers
data_before_handling_outliers_train = data_train.copy()
data_before_handling_outliers_test = data_test.copy()
In [ ]:
# restore point
data_train = data_before_handling_outliers_train.copy()
data_test = data_before_handling_outliers_test.copy()
In [ ]:
# z-score outliers handling
from scipy.stats import zscore
def handle_outliers_zscore(data, columns, threshold=3, plot=False):
    filtered_entries = np.array([True] * len(data))
    outliers_entries = np.array([True] * len(data))

    for column in columns:
        z_scores = np.abs(zscore(data[column]))
        filtered = (z_scores < threshold)
        filtered_entries = np.logical_and(filtered, filtered_entries)
        outliers_entries = ~filtered_entries
        filtered_data = data[filtered_entries]
        outliers_data = data[outliers_entries]

    filtered_data.reset_index(drop=True, inplace=True)
    outliers_data.reset_index(drop=True, inplace=True)

    if plot:
        for column in columns:
            fig, ax = plt.subplots(1, 2, figsize=(8, 4))
            ax[0].set_title(f'Before Outliers in {column}')
            sns.boxplot(data[column], ax=ax[0])
            ax[1].set_title(f'After Outliers in {column}')
            sns.boxplot(filtered_data[column], ax=ax[1])
            plt.tight_layout()
            plt.show()

    data_size = data.shape[0]
    filtered_size = filtered_data.shape[0]
    outliers_size = outliers_data.shape[0]
    pct_outliers = round((outliers_size / data_size) * 100, 2)

    result = pd.Series(data=[data_size, filtered_size, outliers_size, pct_outliers],
                       index=['Before', 'After', 'Outliers', '% Outliers'])
    print(result, '\n')

    return filtered_data, outliers_data
In [ ]:
# menghapus outlier pada feature income data_train
outlier_cols = ['Income']
data_train, residu_income_train = handle_outliers_zscore(data_train, outlier_cols, 3, True)

# menghapus outlier pada feature Year_Birth data_train
outlier_cols = ['Year_Birth']
data_train, residu_yearbirth_train = handle_outliers_zscore(data_train, outlier_cols, 3, True)
Before        1657.00
After         1650.00
Outliers         7.00
% Outliers       0.42
dtype: float64 

Before        1650.00
After         1649.00
Outliers         1.00
% Outliers       0.06
dtype: float64 

In [ ]:
residu_income_train.sort_values(by='Income', ascending=False)
Out[ ]:
Year_Birth Education Marital_Status Income Kidhome Teenhome Dt_Customer Recency MntWines MntFruits ... NumCatalogPurchases NumStorePurchases NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain Response
0 1977 Graduation Together 666666.0 1 0 2013-06-02 23 9 14 ... 1 3 6 0 0 0 0 0 0 0
5 1982 PhD Married 160803.0 0 0 2012-08-04 21 55 16 ... 28 1 0 0 0 0 0 0 0 0
6 1971 Master Together 157733.0 1 0 2013-06-04 37 39 1 ... 0 1 1 0 0 0 0 0 0 0
1 1973 PhD Married 157243.0 0 1 2014-03-01 98 20 2 ... 22 0 0 0 0 0 0 0 0 0
2 1977 Graduation Together 157146.0 0 0 2013-04-29 13 1 0 ... 28 0 1 0 0 0 0 0 0 0
3 1949 PhD Married 156924.0 0 0 2013-08-29 85 2 1 ... 0 0 0 0 0 0 0 0 0 0
4 1975 Graduation Divorced 153924.0 0 0 2014-02-07 81 1 1 ... 0 0 0 0 0 0 0 0 0 0

7 rows × 26 columns

In [ ]:
residu_yearbirth_train.sort_values(by='Year_Birth', ascending=False)
Out[ ]:
Year_Birth Education Marital_Status Income Kidhome Teenhome Dt_Customer Recency MntWines MntFruits ... NumCatalogPurchases NumStorePurchases NumWebVisitsMonth AcceptedCmp3 AcceptedCmp4 AcceptedCmp5 AcceptedCmp1 AcceptedCmp2 Complain Response
0 1899 PhD Together 83532.0 0 0 2013-09-26 36 755 144 ... 6 4 1 0 0 1 0 0 0 0

1 rows × 26 columns

4.5 Handling Invalid Values

In [ ]:
# handling invalid values
# mengubah value pada feature education
data_train['Education'].replace({'Basic':'Graduation','2n Cycle':'Master'}, inplace=True)
data_test['Education'].replace({'Basic':'Graduation','2n Cycle':'Master'}, inplace=True)

# mengubah value pada feature marital_status
data_train['Marital_Status'].replace({'YOLO':'Single','Absurd':'Single','Alone':'Single',
                                'Widow':'Divorced','Together':'Married'}, inplace=True)
data_train['Marital_Status'].replace({'YOLO':'Single','Absurd':'Single','Alone':'Single',
                                'Widow':'Divorced','Together':'Married'}, inplace=True)

4.6 Feature Extraction

In [ ]:
# backup dataset sebelum melakukan feature extraction
data_before_extraction_train = data_train.copy()
data_before_extraction_test = data_test.copy()
data_before_extraction = data.copy()
In [ ]:
# restore point
data_train = data_before_extraction_train.copy()
data_test = data_before_extraction_test.copy()
data = data_before_extraction.copy()
In [ ]:
data_train['feature'] = 'train'
data_test['feature'] = 'test'
data = pd.concat([data_train,data_test]).reset_index(drop=True)
In [ ]:
# membuat feature baru berdasarkan status hubungan
marital = {
    'Single': 'Not in relationship',
    'Together': 'In relationship',
    'Married': 'In relationship',
    'Divorced': 'Not in relationship',
    'Widow': 'Not in relationship',
    'Alone': 'Not in relationship',
    'Absurd': 'Not in relationship',
    'YOLO': 'Not in relationship'
}
data['Relationship_Status'] = data['Marital_Status'].map(marital)

# membuat feature baru total_children dari penjumlahan feature kidhome dan teenhome
data['Total_Children'] = data['Kidhome'] + data['Teenhome']

# membuat feature baru berdasarkan jumlah anggota keluarga
def fam_size(x):
    if x['Relationship_Status'] == 'Not in relationship':
        result = 1 + x['Teenhome'] + x['Kidhome']
    elif x['Relationship_Status'] == 'In relationship':
        result = 2 + x['Teenhome'] + x['Kidhome']
    return result
data['Family_Size'] = data.apply(fam_size, axis=1)

# membuat feature baru berdasarkan tanggal bergabung dan diasumsikan data dikumpulkan pada awal juli 2014
data['Customer_Lifespan'] = (pd.to_datetime('2014-07-01') - data['Dt_Customer']).dt.days

# ekstraksi feature Datetime menjadi feature baru
data['Year'] = data['Dt_Customer'].dt.year
data.drop(['Dt_Customer'],axis=1,inplace=True)

# membuat feature baru total purchase, total spending, dan total offers
data['Total_Purchase'] = data.apply(lambda x: x[purchase_cols[:-1]].sum(), axis=1)
data['Total_Spending'] = data.apply(lambda x: x[spending_cols].sum(), axis=1)
data['Total_Offers'] = data.apply(lambda x: x[campaign_cols[:-1]].sum(), axis=1)

# membuat feature baru untuk menghitung ratio spending dengan total pembelian, dan ratio pembelian diskon dengan total pembelian
data['Spending_Purchase_Ratio'] = data['Total_Spending']/data['Total_Purchase']
data['Deal_Purchase_Ratio'] = data['NumDealsPurchases']/data['Total_Purchase']

# membuat feature baru accept one campaign and more than one campaign
data['at_least_one_campaign'] = data.apply(lambda x: 1 if x[campaign_cols[:-1]].sum() == 1 else 0, axis=1)
data['more_one_campaign'] = data.apply(lambda x: 1 if x[campaign_cols[:-1]].sum() > 1 else 0, axis=1)

# membuat feature baru untuk primary_needs
def categorize_needs(row):
    primary_needs = row['MntFruits'] + row['MntMeatProducts'] + row['MntFishProducts']
    secondary_needs = row['MntWines'] + row['MntGoldProds'] + row['MntSweetProducts']

    if primary_needs > secondary_needs:
        return 'primary_needs'
    else:
        return 'secondary_needs'

data['primary_needs'] = data.apply(categorize_needs, axis=1)

# identifikasi dan handling inf value dari 'Spending_Purchase_Ratio' dan 'Deal_Purchase_Ratio'
data.replace([np.inf, -np.inf], np.nan, inplace=True)
data.dropna(axis=0, inplace=True)
data.reset_index(drop=True, inplace=True)
data.head()
Out[ ]:
Year_Birth Education Marital_Status Income Kidhome Teenhome Recency MntWines MntFruits MntMeatProducts ... Customer_Lifespan Year Total_Purchase Total_Spending Total_Offers Spending_Purchase_Ratio Deal_Purchase_Ratio at_least_one_campaign more_one_campaign primary_needs
0 1950 Graduation Single 16813.0 0 0 49 4 8 11 ... 347 2013 6 50 1 8.333333 0.166667 1 0 primary_needs
1 1963 Master Single 64191.0 0 1 30 420 15 186 ... 525 2013 24 825 0 34.375000 0.125000 0 0 secondary_needs
2 1971 PhD Married 71969.0 0 1 59 1000 0 76 ... 623 2012 19 1086 1 57.157895 0.157895 1 0 secondary_needs
3 1968 PhD Married 29187.0 1 0 43 26 0 6 ... 418 2013 5 34 0 6.800000 0.200000 0 0 secondary_needs
4 1969 Graduation Married 4428.0 0 1 0 16 4 12 ... 269 2013 25 359 0 14.360000 0.000000 0 0 secondary_needs

5 rows × 39 columns

In [ ]:
# rfm encode

'''
Champions: Pelanggan dengan RFM score tertinggi.
Loyal Customers: Pelanggan dengan frekuensi tinggi dan nilai monetary tinggi.
At-Risk Customers: Pelanggan dengan recency rendah dan frekuensi rendah.
New Customers: Pelanggan dengan recency tinggi dan frekuensi rendah.
'''

# membuat feature baru category rfm score (champions, loyal, at risk, new)
rfm = pd.DataFrame()
rfm['Recency'] = data['Recency']
rfm['Frequency'] = data['Total_Purchase']
rfm['Monetary'] = data['Total_Spending']
rfm.head()
Out[ ]:
Recency Frequency Monetary
0 49 6 50
1 30 24 825
2 59 19 1086
3 43 5 34
4 0 25 359
In [ ]:
# kalkulasi score berdasarkan quantile masing-masing feature
rfm['recency_score'] = pd.qcut(rfm['Recency'], q=[0, 0.25, 0.5, 0.75, 1], labels=[4, 3, 2, 1])
rfm['frequency_score'] = pd.qcut(rfm['Frequency'], q=[0, 0.25, 0.5, 0.75, 1], labels=[1, 2, 3, 4])
rfm['monetary_score'] = pd.qcut(rfm['Monetary'], q=[0, 0.25, 0.5, 0.75, 1], labels=[1, 2, 3, 4])

# histplot rfm features
fig, axes = plt.subplots(1, 3, figsize=(12, 4))
sns.histplot(rfm['recency_score'], kde=True, ax=axes[0], color='#e9c369')
axes[0].set_title('Recency Score')

sns.histplot(rfm['frequency_score'], kde=True, ax=axes[1], color='#e9c369')
axes[1].set_title('Frequency Score')

sns.histplot(rfm['monetary_score'], kde=True, ax=axes[2], color='#e9c369')
axes[2].set_title('Monetary Score')

plt.tight_layout()
plt.show()
In [ ]:
# histplot rfm score
rfm['rfm_score'] = rfm['recency_score'].astype('int') + rfm['frequency_score'].astype('int') + rfm['monetary_score'].astype('int')
sns.histplot(rfm['rfm_score'], kde=True, bins=10, color='#e9c369')
Out[ ]:
<Axes: xlabel='rfm_score', ylabel='Count'>
In [ ]:
'''scoring rfm
0-4 new cust
5-7 at risk cust
8-10 loyal cust
10-12 champions

'''
rfm['RFM_Cat'] = pd.cut(rfm['rfm_score'], bins=[0,4,7,10,12], labels=['new cust','at risk cust','loyal cust','champions'])
ax = sns.countplot(x=rfm['RFM_Cat'], palette=sns.color_palette())
for p in ax.patches:
    ax.annotate(f'{p.get_height():.0f}', (p.get_x() + p.get_width() / 2., p.get_height()),
    ha='center', va='center', xytext=(0, 5), textcoords='offset points')
In [ ]:
# concat dataframe data dengan rfm
data = pd.concat([data, rfm['RFM_Cat']], axis=1)

4.7 Feature Encoding

In [ ]:
# backup dataset sebelum melakukan feature enconding
data_before_encoding = data.copy()
data_before_encoding.to_csv('data_before_encoding.csv', index=False)
In [ ]:
# restore point
data = data_before_encoding.copy()
In [ ]:
from sklearn.preprocessing import OrdinalEncoder
# encoding education
edu = {'Graduation': 1, 'Master': 2, 'PhD': 3}
oe_edu = OrdinalEncoder(categories=[list(edu.keys())])
data['Education'] = oe_edu.fit_transform(data[['Education']])

# encoding marital_status
marital_mapping = {'Single': 1, 'Married': 2, 'Divorced': 3}
data['Marital_Status'] = data['Marital_Status'].map(marital_mapping)

# encoding relationship_status
rel_mapping = {'Not in relationship': 0,
               'In relationship': 1}
data['Relationship_Status'] = data['Relationship_Status'].map(
    rel_mapping)

# encoding primary_needs
pr_mapping = {'primary_needs': 0,
              'secondary_needs': 1}
data['primary_needs'] = data['primary_needs'].map(pr_mapping)

# encoding rfm_cat
rfm_mapping = {'new cust': 1, 'at risk cust': 2,
               'loyal cust': 3, 'champions': 4}
oe_rfm = OrdinalEncoder(categories=[list(rfm_mapping.keys())])
data['RFM_Cat'] = oe_rfm.fit_transform(data[['RFM_Cat']])
In [ ]:
# splitting dataset / restore data_train dan data_test
data_train = data[data['feature'] == 'train'].copy()
data_test = data[data['feature'] == 'test'].copy()

data_train.drop('feature', axis=1, inplace=True)
data_test.drop('feature', axis=1, inplace=True)

data_train.reset_index(drop=True, inplace=True)
data_test.reset_index(drop=True, inplace=True)

4.8 Feature Transformation

In [ ]:
# backup data sebelum transformation
data_before_transform_train = data_train.copy()
data_before_transform_test = data_test.copy()
In [ ]:
# restore point data train dan data test
data_train = data_before_transform_train.copy()
data_test = data_before_transform_test.copy()
In [ ]:
from scipy.stats import skew, kurtosis
def assess_distribution_multi(data, features, alpha=0.05, show_plot=False):

    num_features = len(features)
    num_cols = min(4, num_features)  # Maksimal 4 Feature dalam satu baris
    num_rows = (num_features - 1) // num_cols + 1  # Jumlah baris yang dibutuhkan

    if show_plot:
        fig, axes = plt.subplots(num_rows, num_cols, figsize=(16, 4 * num_rows))
        axes = axes.flatten()

    skew_type_list = []  # Tambahkan inisialisasi list
    skew_val_list = []   # Tambahkan inisialisasi list
    normalization_advice_list = []  # Tambahkan inisialisasi list
    transform_columns = []  # Tambahkan inisialisasi list

    results = []
    for i, feature_name in enumerate(features):
        feature_data = data[feature_name].dropna(axis=0)
        skew_val = round(skew(feature_data, nan_policy="omit"), 3)

        if (len(feature_data) > 0):  # Tambahkan kondisi untuk memastikan ada data sebelum melanjutkan
            mean = round(feature_data.mean(), 3)
            median = feature_data.median()
            mode = feature_data.mode()[0]
            q1 = feature_data.quantile(q=0.25)
            q3 = feature_data.quantile(q=0.75)

            if (mean == median == mode) or (-0.2 < skew_val < 0.2):
                skew_type = "Normal Distribution (Symmetric)"
                normalization_advice = "No Transformation"

            elif mean < median < mode:
                skew_type = "Negatively Skewed"
                normalization_advice = "MinMaxScaler"
                if skew_val <= -1:
                    skew_type = "Highly Negatively Skewed"
                    normalization_advice = "PowerTransformer/Log"
            else:
                skew_type = "Positively Skewed"
                normalization_advice = "MinMaxScaler"
                if skew_val >= 1:
                    skew_type = "Highly Positively Skewed"
                    normalization_advice = "PowerTransformer/Log"

            skew_type_list.append(skew_type)
            skew_val_list.append(skew_val)
            normalization_advice_list.append(normalization_advice)

            # Tambahkan fitur ke dalam daftar yang perlu ditransformasi atau standarisasi
            if normalization_advice != "No Transformation":
                transform_columns.append(feature_name)

            # Gambar histplot dalam subplot
            if show_plot:
                sns.histplot(feature_data, kde=True, ax=axes[i])
                axes[i].set_title(f'Histogram for {feature_name}')

    if show_plot:
        # Sembunyikan subplot yang tidak terpakai
        for j in range(i + 1, len(axes)):
            axes[j].axis('off')

        plt.tight_layout()
        plt.show()

    # Buat DataFrame hasil penilaian distribusi
    result_df = pd.DataFrame({
        'Feature': features,
        'Skewness': skew_val_list,
        'Type of Distribution': skew_type_list,
        'Normalization Advice': normalization_advice_list
    }).set_index('Feature')

    return result_df, transform_columns
In [ ]:
# memilih feature yang akan ditransformasikan
cols_to_transform = data_train.drop('Response', axis=1).columns
cols_to_transform = sorted(cols_to_transform)
In [ ]:
# identifikasi feature sebelum ditransformasi
result_table = assess_distribution_multi(data_train, cols_to_transform, show_plot=True)[0]
result_table
Out[ ]:
Skewness Type of Distribution Normalization Advice
Feature
AcceptedCmp1 3.531 Highly Positively Skewed PowerTransformer/Log
AcceptedCmp2 8.911 Highly Positively Skewed PowerTransformer/Log
AcceptedCmp3 3.188 Highly Positively Skewed PowerTransformer/Log
AcceptedCmp4 3.341 Highly Positively Skewed PowerTransformer/Log
AcceptedCmp5 3.254 Highly Positively Skewed PowerTransformer/Log
Complain 9.151 Highly Positively Skewed PowerTransformer/Log
Customer_Lifespan -0.011 Normal Distribution (Symmetric) No Transformation
Deal_Purchase_Ratio 0.831 Positively Skewed MinMaxScaler
Education 0.628 Positively Skewed MinMaxScaler
Family_Size 0.080 Normal Distribution (Symmetric) No Transformation
Income 0.023 Normal Distribution (Symmetric) No Transformation
Kidhome 0.615 Positively Skewed MinMaxScaler
Marital_Status 0.012 Normal Distribution (Symmetric) No Transformation
MntFishProducts 1.907 Highly Positively Skewed PowerTransformer/Log
MntFruits 2.062 Highly Positively Skewed PowerTransformer/Log
MntGoldProds 1.877 Highly Positively Skewed PowerTransformer/Log
MntMeatProducts 1.836 Highly Positively Skewed PowerTransformer/Log
MntSweetProducts 2.082 Highly Positively Skewed PowerTransformer/Log
MntWines 1.223 Highly Positively Skewed PowerTransformer/Log
NumCatalogPurchases 1.445 Highly Positively Skewed PowerTransformer/Log
NumDealsPurchases 2.415 Highly Positively Skewed PowerTransformer/Log
NumStorePurchases 0.722 Positively Skewed MinMaxScaler
NumWebPurchases 1.386 Highly Positively Skewed PowerTransformer/Log
NumWebVisitsMonth 0.367 Negatively Skewed MinMaxScaler
RFM_Cat -0.035 Normal Distribution (Symmetric) No Transformation
Recency 0.014 Normal Distribution (Symmetric) No Transformation
Relationship_Status -0.640 Positively Skewed MinMaxScaler
Spending_Purchase_Ratio 1.333 Highly Positively Skewed PowerTransformer/Log
Teenhome 0.412 Positively Skewed MinMaxScaler
Total_Children 0.407 Positively Skewed MinMaxScaler
Total_Offers 2.681 Highly Positively Skewed PowerTransformer/Log
Total_Purchase 0.244 Positively Skewed MinMaxScaler
Total_Spending 0.885 Positively Skewed MinMaxScaler
Year -0.043 Normal Distribution (Symmetric) No Transformation
Year_Birth -0.088 Normal Distribution (Symmetric) No Transformation
at_least_one_campaign 2.002 Highly Positively Skewed PowerTransformer/Log
more_one_campaign 3.636 Highly Positively Skewed PowerTransformer/Log
primary_needs -1.251 Positively Skewed MinMaxScaler
In [ ]:
log_cols = list(result_table[result_table['Normalization Advice'] == 'PowerTransformer/Log'].index)
norm_cols = list(result_table[result_table['Normalization Advice'].isin(['MinMaxScaler','No Transformation'])].index)

print(f'log transformation: {log_cols}')
print(f'normalisasi: {norm_cols}')
log transformation: ['AcceptedCmp1', 'AcceptedCmp2', 'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'Complain', 'MntFishProducts', 'MntFruits', 'MntGoldProds', 'MntMeatProducts', 'MntSweetProducts', 'MntWines', 'NumCatalogPurchases', 'NumDealsPurchases', 'NumWebPurchases', 'Spending_Purchase_Ratio', 'Total_Offers', 'at_least_one_campaign', 'more_one_campaign']
normalisasi: ['Customer_Lifespan', 'Deal_Purchase_Ratio', 'Education', 'Family_Size', 'Income', 'Kidhome', 'Marital_Status', 'NumStorePurchases', 'NumWebVisitsMonth', 'RFM_Cat', 'Recency', 'Relationship_Status', 'Teenhome', 'Total_Children', 'Total_Purchase', 'Total_Spending', 'Year', 'Year_Birth', 'primary_needs']
In [ ]:
# menggunakan powertransformer untuk transformasi features pada log_cols
from sklearn.preprocessing import PowerTransformer

# melakukan transformasi
scaler = PowerTransformer(method='yeo-johnson')
data_train[log_cols] = scaler.fit_transform(data_train[log_cols])
data_test[log_cols] = scaler.transform(data_test[log_cols])

# menampilkan distribusi fitur-fitur setelah PowerTransform
plt.figure(figsize=(12, 6))
plt.boxplot(data_train[log_cols], vert=False, labels=data_train[log_cols].columns)
plt.title('Distribusi Fitur setelah PowerTransform')
plt.xlabel('Nilai Transformasi')
plt.ylabel('Fitur')
plt.show()
In [ ]:
# menggunakan powertransformer untuk transformasi features
from sklearn.preprocessing import MinMaxScaler

minmax = MinMaxScaler()
data_train[norm_cols] = minmax.fit_transform(data_train[norm_cols])
data_test[norm_cols] = minmax.transform(data_test[norm_cols])

# menampilkan distribusi fitur-fitur setelah Normalisasi
plt.figure(figsize=(12, 6))
plt.boxplot(data_train[norm_cols], vert=False,
            labels=data_train[norm_cols].columns)
plt.title('Distribusi Fitur setelah Normalisasi')
plt.xlabel('Nilai Normalisasi')
plt.ylabel('Fitur')
plt.show()

4.9 Feature Selection

In [ ]:
# backup
data_before_feature_selection_train = data_train.copy()
data_before_feature_selection_test = data_test.copy()
In [ ]:
# restore
data_train = data_before_feature_selection_train.copy()
data_train.head()
Out[ ]:
Year_Birth Education Marital_Status Income Kidhome Teenhome Recency MntWines MntFruits MntMeatProducts ... Year Total_Purchase Total_Spending Total_Offers Spending_Purchase_Ratio Deal_Purchase_Ratio at_least_one_campaign more_one_campaign primary_needs RFM_Cat
0 0.178571 0.0 0.0 0.134665 0.0 0.0 0.494949 -1.523740 -0.004248 -1.040166 ... 0.5 0.119048 0.016687 1.932145 -0.991174 0.166667 2.416231 -0.256859 0.0 0.333333
1 0.410714 0.5 0.0 0.557668 0.0 0.5 0.303030 0.756515 0.356628 0.698862 ... 0.5 0.547619 0.324593 -0.512632 0.471277 0.125000 -0.413868 -0.256859 1.0 0.666667
2 0.553571 1.0 0.5 0.627112 0.0 0.5 0.595960 1.453882 -1.447016 0.121258 ... 0.0 0.428571 0.428288 1.932145 1.036242 0.157895 2.416231 -0.256859 1.0 0.666667
3 0.500000 1.0 0.5 0.245143 0.5 0.0 0.434343 -0.859859 -1.447016 -1.365005 ... 0.5 0.095238 0.010330 -0.512632 -1.182550 0.200000 -0.413868 -0.256859 1.0 0.333333
4 0.517857 0.0 0.5 0.024088 0.0 0.5 0.000000 -1.062296 -0.380042 -0.991477 ... 0.5 0.571429 0.139452 -0.512632 -0.453045 0.000000 -0.413868 -0.256859 1.0 0.666667

5 rows × 39 columns

In [ ]:
# memisahkan feature berdasarkan tipe data cat/object dan numerik
num_cols = []
cat_cols = []

# melakukan perulangan setiap feature sesuai dengan tipe data
for col in data_before_encoding.drop(['Response','feature'], axis=1).columns:
    if data_before_encoding[col].dtype == 'int64' or data_before_encoding[col].dtype == 'float64':
        num_cols.append(col)
    elif data_before_encoding[col].dtype == 'object' or data_before_encoding[col].dtype == 'category':
        cat_cols.append(col)

# print hasilnya yang disimpan pada masing-masing feature
print(f'Total Numeric Columns : {len(num_cols)}')
print(f'Total Object Columns  : {len(cat_cols)}')
Total Numeric Columns : 33
Total Object Columns  : 5

4.9.1 Based on Anova (Numerical-Categorical)

In [ ]:
# memisahkan X dan y sebagai feature dan target
X_train = data_train.drop('Response', axis=1)
y_train = data_train['Response']

X_test = data_test.drop('Response', axis=1)
y_test = data_test['Response']
In [ ]:
# melakukan features selection menggunakan f_classif (anova)
from sklearn.feature_selection import f_classif

# menghitung nilai F-statistic dan p-value untuk setiap feature
f_statistic, p_values = f_classif(X_train[num_cols], y_train)

# menentukan tingkat signifikansi (alpha)
alpha = 0.05

# memilih feature yang memiliki p-value kurang dari 0.05 (95%, tingkat signifikansi umumnya digunakan)
selected_features_indices = np.where(p_values < alpha)[0]
selected_feature_names = X_train[num_cols].columns[selected_features_indices].tolist()

# membuat DataFrame dengan informasi F-statistic dan p-value untuk fitur yang dipilih
selected_features_anova = pd.DataFrame({
    'Feature': selected_feature_names,
    'F_Statistic': f_statistic[selected_features_indices],
    'P_Value': p_values[selected_features_indices]
})
# mengurutkan DataFrame berdasarkan F-statistic secara descending
selected_features_anova = selected_features_anova.sort_values(by='F_Statistic', ascending=False)

# menampilkan feature yang dipilih beserta skor F-statistic dalam bentuk tabel
print("\nTabel Feature yang Dipilih:")
print(selected_features_anova)

# menampilkan skor dalam bentuk plot
plt.figure(figsize=(12, 6))
plt.bar(selected_features_anova['Feature'], -np.log10(selected_features_anova['P_Value']), color='#e9c369')
plt.axhline(-np.log10(alpha), color='red', linestyle='dashed', linewidth=2, label=f'Significance Level ({alpha})')
plt.title('Feature Importance based on ANOVA F-Statistic')
plt.xlabel('Features')
plt.ylabel('-log10(p-value)')
plt.xticks(rotation=45, ha='right')
plt.legend()
plt.show()
Tabel Feature yang Dipilih:
                    Feature  F_Statistic       P_Value
23             Total_Offers   292.373006  1.835470e-60
27        more_one_campaign   246.157183  8.332075e-52
14             AcceptedCmp5   212.448003  2.363466e-45
15             AcceptedCmp1   179.074224  7.643053e-39
22           Total_Spending   126.699997  2.281261e-28
12             AcceptedCmp3   118.700317  9.721559e-27
11      NumCatalogPurchases   102.398177  2.159944e-23
24  Spending_Purchase_Ratio    98.001782  1.750366e-22
18              Family_Size    95.648147  5.379007e-22
6           MntMeatProducts    93.846780  1.271728e-21
3                   Recency    80.756569  6.823586e-19
26    at_least_one_campaign    66.710987  6.192657e-16
9              MntGoldProds    65.667004  1.030405e-15
4                  MntWines    64.506086  1.816035e-15
19        Customer_Lifespan    59.696130  1.911319e-14
17           Total_Children    59.358758  2.255200e-14
13             AcceptedCmp4    54.314835  2.691016e-13
10          NumWebPurchases    53.451504  4.118075e-13
0                    Income    52.134165  7.887214e-13
20                     Year    46.463783  1.305505e-11
21           Total_Purchase    45.046183  2.639741e-11
2                  Teenhome    44.137642  4.147375e-11
16             AcceptedCmp2    39.731354  3.733437e-10
5                 MntFruits    30.385701  4.104349e-08
8          MntSweetProducts    28.313325  1.173656e-07
25      Deal_Purchase_Ratio    20.639837  5.946699e-06
7           MntFishProducts    18.802021  1.538053e-05
1                   Kidhome    14.569459  1.400886e-04
In [ ]:
X_train_anova = X_train.loc[:,selected_feature_names]
X_test_anova = X_test.loc[:,selected_feature_names]

4.9.2 Based on Chi-Square (Categorical-Categorical)

In [ ]:
# melakukan features selection menggunakan chi-square test
from sklearn.feature_selection import chi2
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# menghitung chi-square statistic dan p-value untuk setiap feature
chi2_stat, p_values_chi2 = chi2(X_train[cat_cols], y_train)

# menentukan tingkat signifikansi (alpha)
alpha = 0.05

# memilih feature yang memiliki p-value kurang dari 0.05 (95% confidence level)
selected_features_chi2_indices = np.where(p_values_chi2 < alpha)[0]
selected_features_chi2_names = X_train[cat_cols].columns[selected_features_chi2_indices].tolist(
)

# membuat DataFrame dengan informasi Chi-statistic dan p-value untuk fitur yang dipilih
selected_features_chi = pd.DataFrame({
    'Feature': selected_features_chi2_names,
    'Chi_Statistic': chi2_stat[selected_features_chi2_indices],
    'P_Value': p_values_chi2[selected_features_chi2_indices]
})
# mengurutkan DataFrame berdasarkan Chi-statistic secara descending
selected_features_chi = selected_features_chi.sort_values(
    by='Chi_Statistic', ascending=False)

# menampilkan feature yang dipilih beserta skor Chi-statistic dalam bentuk tabel
print("\nTabel Feature yang Dipilih:")
print(selected_features_chi)

# menampilkan skor dalam bentuk plot
plt.figure(figsize=(12, 6))
plt.bar(selected_features_chi['Feature'], -
        np.log10(selected_features_chi['P_Value']), color='#e9c369')
plt.axhline(-np.log10(alpha), color='red', linestyle='dashed',
            linewidth=2, label=f'Significance Level ({alpha})')
plt.title('Feature Importance based on Chi-square Statistic')
plt.xlabel('Features')
plt.ylabel('-log10(p-value)')
plt.xticks(rotation=0, ha='center')
plt.ylim(0, 10)  # Mengatur skala ylim dari 0 hingga 10
plt.legend()
plt.show()
Tabel Feature yang Dipilih:
               Feature  Chi_Statistic   P_Value
2              RFM_Cat      19.379965  0.000011
1  Relationship_Status      13.154564  0.000287
0            Education       4.374336  0.036484
In [ ]:
X_train_chi2 = X_train.loc[:,selected_features_chi2_names]
X_test_chi2 = X_test.loc[:,selected_features_chi2_names]
In [ ]:
# concat selected features from anova dan chi2
X_train = pd.concat([X_train_anova, X_train_chi2], axis=1)
X_test = pd.concat([X_test_anova, X_test_chi2], axis=1)

4.9.3 Redundancy Analysis

Variance Inflation Factor (VIF)¶

VIF mengukur seberapa jauh variabilitas suatu variabel independen dapat dijelaskan oleh variabel independen lain dalam model. Sebagai aturan umum, nilai VIF yang tinggi (biasanya di atas 10) menunjukkan adanya masalah multikolinearitas, yang dapat mempengaruhi interpretasi model dan membuat estimasi koefisien menjadi tidak stabil.

Langkah-langkah Feature Selection dengan VIF:¶
  1. Seleksi Awal Fitur:

    • Lakukan seleksi awal fitur yang ingin dievaluasi dalam model regresi logistik.
  2. Engineering Fitur (Opsional):

    • Jika diperlukan, Anda dapat membuat fitur baru atau melakukan engineering pada fitur yang ada.
  3. Perhitungan VIF:

    • Hitung VIF untuk setiap variabel independen dalam model regresi logistik. Biasanya, perangkat lunak statistik seperti Python (menggunakan pustaka seperti Statsmodels atau Scikit-learn) atau R menyediakan fungsi untuk menghitung VIF.
  4. Evaluasi VIF:

    • Evaluasi nilai VIF untuk setiap variabel. Jika nilai VIF melebihi batas tertentu (misalnya, 10), pertimbangkan untuk menghapus variabel tersebut.
  5. Pemodelan Ulang:

    • Hapus variabel dengan nilai VIF tinggi dan refit model regresi logistik.
  6. Ulangi Langkah 3-5:

    • Ulangi proses perhitungan VIF dan pemodelan ulang sampai tidak ada lagi variabel dengan nilai VIF yang tinggi.
  7. Evaluasi Kinerja:

    • Evaluasi kinerja model regresi logistik yang telah di-fit ulang setelah seleksi fitur menggunakan VIF.
In [ ]:
# default feature selection
from statsmodels.stats.outliers_influence import variance_inflation_factor

def calculate_vif(df):
    vif_data = pd.DataFrame()
    vif_data["Feature"] = df.columns
    vif_data["VIF"] = [variance_inflation_factor(
        df.values, i) for i in range(df.shape[1])]
    return vif_data

vif_results = calculate_vif(X_train)
print(f'Avg. VIF Score: {(sum(vif_results["VIF"]/len(vif_results)))}')
vif_results
Avg. VIF Score: inf
Out[ ]:
Feature VIF
0 Income 3.000721e+01
1 Kidhome inf
2 Teenhome inf
3 Recency 7.906623e+00
4 MntWines 1.536519e+01
5 MntFruits 2.799495e+00
6 MntMeatProducts 1.068134e+01
7 MntFishProducts 2.946718e+00
8 MntSweetProducts 2.689423e+00
9 MntGoldProds 2.795796e+00
10 NumWebPurchases 4.477282e+00
11 NumCatalogPurchases 6.486516e+00
12 AcceptedCmp3 4.820741e+01
13 AcceptedCmp4 4.646552e+01
14 AcceptedCmp5 4.511825e+01
15 AcceptedCmp1 4.010818e+01
16 AcceptedCmp2 5.816116e+00
17 Total_Children inf
18 Family_Size inf
19 Customer_Lifespan 1.606089e+01
20 Year 1.268764e+01
21 Total_Purchase 5.257854e+01
22 Total_Spending 1.959583e+01
23 Total_Offers 2.266641e+07
24 Spending_Purchase_Ratio 2.694432e+01
25 Deal_Purchase_Ratio 1.233535e+01
26 at_least_one_campaign 1.684281e+07
27 more_one_campaign 8.183600e+06
28 Education 2.049095e+00
29 Relationship_Status inf
30 RFM_Cat 2.594126e+01
In [ ]:
drop_features = ['Total_Children', 'Kidhome', 'Teenhome', 'at_least_one_campaign', 'more_one_campaign', 'Family_Size', 'Relationship_Status',
                 'Income', 'Year', 'Total_Purchase', 'Spending_Purchase_Ratio', 'Total_Spending', 'Deal_Purchase_Ratio', 'Total_Offers','MntMeatProducts','MntWines']
In [ ]:
# vif score setelah menghapus beberapa feature
vif_results = calculate_vif(X_train.drop(drop_features, axis=1))
print(f'Avg. VIF Score: {(sum(vif_results["VIF"]/len(vif_results)))}')
vif_results
Avg. VIF Score: 2.2793386425396354
Out[ ]:
Feature VIF
0 Recency 2.562749
1 MntFruits 2.510120
2 MntFishProducts 2.662851
3 MntSweetProducts 2.489294
4 MntGoldProds 2.186144
5 NumWebPurchases 1.853393
6 NumCatalogPurchases 3.031351
7 AcceptedCmp3 1.103208
8 AcceptedCmp4 1.280283
9 AcceptedCmp5 1.372765
10 AcceptedCmp1 1.306924
11 AcceptedCmp2 1.119148
12 Customer_Lifespan 4.230985
13 Education 1.960772
14 RFM_Cat 4.520092
In [ ]:
# membuat variabel untuk feature selection
selected_features = X_train.drop(drop_features, axis=1).columns
selected_features = sorted(list(selected_features))
selected_features
Out[ ]:
['AcceptedCmp1',
 'AcceptedCmp2',
 'AcceptedCmp3',
 'AcceptedCmp4',
 'AcceptedCmp5',
 'Customer_Lifespan',
 'Education',
 'MntFishProducts',
 'MntFruits',
 'MntGoldProds',
 'MntSweetProducts',
 'NumCatalogPurchases',
 'NumWebPurchases',
 'RFM_Cat',
 'Recency']

4.10 Imbalance Handling

In [ ]:
# backup sebelum handling imbalance
X_before_handling_imbalance_training = X_train.copy()
y_before_handling_imbalance_training = y_train.copy()
In [ ]:
# restore point
X_train = X_before_handling_imbalance_training.copy()
y_train = y_before_handling_imbalance_training.copy()
In [ ]:
X_train = X_train[selected_features]
X_test = X_test[selected_features]
In [ ]:
# melakukan imbalace handling pada target
from imblearn.over_sampling import SMOTE

# menampikan jumlah kelas sebelum oversampling
print("Jumlah kelas sebelum oversampling:")
print("Kelas 0:", sum(y_train == 0))
print("Kelas 1:", sum(y_train == 1))

# melakukan oversampling dengan SMOTE
smote = SMOTE(random_state=42)
X_resampled, y_resampled = smote.fit_resample(X_train, y_train)

# menampilkan jumlah kelas setelah oversampling
print("\nJumlah kelas setelah oversampling:")
print("Kelas 0:", sum(y_resampled == 0))
print("Kelas 1:", sum(y_resampled == 1))
Jumlah kelas sebelum oversampling:
Kelas 0: 1397
Kelas 1: 251

Jumlah kelas setelah oversampling:
Kelas 0: 1397
Kelas 1: 1397

5. Machine Learning Modelling & Evaluation

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5.1 Modelling

In [ ]:
# mendefinisikan X (features) dan y (target), dimana X dan y hasil dari imbalance handling sebelumnya
X_train = X_resampled
y_train = y_resampled
In [ ]:
from sklearn.model_selection import learning_curve, cross_val_score, GridSearchCV
from sklearn.metrics import precision_score, accuracy_score, make_scorer, log_loss
from sklearn.svm import SVC
from xgboost import XGBClassifier
from sklearn.ensemble import AdaBoostClassifier
In [ ]:
X_train.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2794 entries, 0 to 2793
Data columns (total 15 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   AcceptedCmp1         2794 non-null   float64
 1   AcceptedCmp2         2794 non-null   float64
 2   AcceptedCmp3         2794 non-null   float64
 3   AcceptedCmp4         2794 non-null   float64
 4   AcceptedCmp5         2794 non-null   float64
 5   Customer_Lifespan    2794 non-null   float64
 6   Education            2794 non-null   float64
 7   MntFishProducts      2794 non-null   float64
 8   MntFruits            2794 non-null   float64
 9   MntGoldProds         2794 non-null   float64
 10  MntSweetProducts     2794 non-null   float64
 11  NumCatalogPurchases  2794 non-null   float64
 12  NumWebPurchases      2794 non-null   float64
 13  RFM_Cat              2794 non-null   float64
 14  Recency              2794 non-null   float64
dtypes: float64(15)
memory usage: 327.5 KB
In [ ]:
def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None, n_jobs=None, train_sizes=np.linspace(.1, 1.0, 5)):
    fig, axes = plt.subplots(1, 2, figsize=(14, 5))

    # Learning Curve for Precision
    axes[0].set_title(f"{title} - Precision")
    if ylim is not None:
        axes[0].set_ylim(*ylim)
    axes[0].set_xlabel("Training examples")
    axes[0].set_ylabel("Score")

    train_sizes, train_scores, test_scores = learning_curve(
        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes, scoring=make_scorer(precision_score, average='weighted'))

    train_scores_mean = np.mean(train_scores, axis=1)
    train_scores_std = np.std(train_scores, axis=1)
    test_scores_mean = np.mean(test_scores, axis=1)
    test_scores_std = np.std(test_scores, axis=1)

    axes[0].grid()
    axes[0].fill_between(train_sizes, train_scores_mean - train_scores_std,
                         train_scores_mean + train_scores_std, alpha=0.1,
                         color="r")
    axes[0].fill_between(train_sizes, test_scores_mean - test_scores_std,
                         test_scores_mean + test_scores_std, alpha=0.1, color="g")
    axes[0].plot(train_sizes, train_scores_mean, 'o-', color="r", label="Training score")
    axes[0].plot(train_sizes, test_scores_mean, 'o-', color="g", label="Cross-validation score")
    axes[0].legend(loc="best")

    # Learning Curve for Loss
    axes[1].set_title(f"{title} - Loss")
    if ylim is not None:
        axes[1].set_ylim(*ylim)
    axes[1].set_xlabel("Training examples")
    axes[1].set_ylabel("Loss")

    train_sizes, train_loss, test_loss = learning_curve(
        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes, scoring=make_scorer(log_loss, needs_proba=True))

    train_loss_mean = np.mean(train_loss, axis=1)
    train_loss_std = np.std(train_loss, axis=1)
    test_loss_mean = np.mean(test_loss, axis=1)
    test_loss_std = np.std(test_loss, axis=1)

    axes[1].grid()
    axes[1].fill_between(train_sizes, train_loss_mean - train_loss_std,
                         train_loss_mean + train_loss_std, alpha=0.1,
                         color="r")
    axes[1].fill_between(train_sizes, test_loss_mean - test_loss_std,
                         test_loss_mean + test_loss_std, alpha=0.1, color="g")
    axes[1].plot(train_sizes, train_loss_mean, 'o-', color="r", label="Training loss")
    axes[1].plot(train_sizes, test_loss_mean, 'o-', color="g", label="Cross-validation loss")
    axes[1].legend(loc="best")

    plt.tight_layout()
    return plt
In [ ]:
def models_precision(X_train, y_train, X_test, y_test):
    models = [
        SVC(probability=True, random_state=42),
        XGBClassifier(max_depth=1, random_state=42),
        AdaBoostClassifier(random_state=42)
    ]

    results_list_test = []
    results_list_train = []
    results_list_diff = []
    results_list_diff_pct = []

    for model in models:
        # Cross-validation
        cv_scores = cross_val_score(model, X_train, y_train, cv=5, scoring='precision')
        avg_cv_precision = cv_scores.mean()

        model.fit(X_train, y_train)

        # Learning Curve
        # plot_learning_curve(model, f"{type(model).__name__}", X_train, y_train, cv=5)

        # test set evaluation
        y_pred_test = model.predict(X_test)
        precision_test = precision_score(y_test, y_pred_test, average='weighted')

        results_test = {
            "Model": type(model).__name__,
            "CV Precision": avg_cv_precision,
            "Precision": precision_test
        }
        results_list_test.append(results_test)

        # train set evaluation
        y_pred_train = model.predict(X_train)
        precision_train = precision_score(y_train, y_pred_train, average='weighted')

        results_train = {
            "Model": type(model).__name__,
            "Precision": precision_train
        }
        results_list_train.append(results_train)

        # diff evaluation
        results_diff = {
            "Model": type(model).__name__,
            "Precision_Difference": precision_train - precision_test
        }
        results_list_diff.append(results_diff)

        # diff evaluation pct
        results_diff_pct = {
            "Model": type(model).__name__,
            "Precision_Difference": ((precision_train - precision_test) / precision_train) * 100
        }
        results_list_diff_pct.append(results_diff_pct)

    # dataframe from test and test evaluation
    results_test_df = pd.DataFrame(results_list_test)
    results_test_df.set_index("Model", inplace=True)

    results_train_df = pd.DataFrame(results_list_train)
    results_train_df.set_index("Model", inplace=True)

    # dataframe evaluations diff
    results_diff_df = pd.DataFrame(results_list_diff)
    results_diff_df.set_index("Model", inplace=True)

    # dataframe evaluation diff pct
    results_diff_pct_df = pd.DataFrame(results_list_diff_pct)
    results_diff_pct_df.set_index("Model", inplace=True)

    # new df
    evaluation_df = pd.DataFrame(index=[type(model).__name__ for model in models])
    evaluation_df['CV Precision'] = results_test_df['CV Precision']
    evaluation_df['Precision_Train'] = results_train_df['Precision']
    evaluation_df['Precision_Test'] = results_test_df['Precision']
    evaluation_df['Diff'] = results_diff_df['Precision_Difference']
    evaluation_df['Diff (%)'] = results_diff_pct_df['Precision_Difference']

    return evaluation_df
In [ ]:
def models_accuracy(X_train, y_train, X_test, y_test):
    models = [
        SVC(probability=True, random_state=42),
        XGBClassifier(max_depth=1, random_state=42),
        AdaBoostClassifier(random_state=42)
    ]

    results_list_test = []
    results_list_train = []
    results_list_diff = []
    results_list_diff_pct = []

    for model in models:
        # Cross-validation
        cv_scores = cross_val_score(model, X_train, y_train, cv=5, scoring='accuracy')
        avg_cv_accuracy = cv_scores.mean()

        model.fit(X_train, y_train)

        # Learning Curve
        # plot_learning_curve(model, f"{type(model).__name__}", X_train, y_train, cv=5)

        # test set evaluation
        y_pred_test = model.predict(X_test)
        accuracy_test = accuracy_score(y_test, y_pred_test)

        results_test = {
            "Model": type(model).__name__,
            "CV Accuracy": avg_cv_accuracy,
            "Accuracy": accuracy_test
        }
        results_list_test.append(results_test)

        # train set evaluation
        y_pred_train = model.predict(X_train)
        accuracy_train = accuracy_score(y_train, y_pred_train)

        results_train = {
            "Model": type(model).__name__,
            "Accuracy": accuracy_train
        }
        results_list_train.append(results_train)

        # diff evaluation
        results_diff = {
            "Model": type(model).__name__,
            "Accuracy_Difference": accuracy_train - accuracy_test
        }
        results_list_diff.append(results_diff)

        # diff evaluation pct
        results_diff_pct = {
            "Model": type(model).__name__,
            "Accuracy_Difference": ((accuracy_train - accuracy_test) / accuracy_train) * 100
        }
        results_list_diff_pct.append(results_diff_pct)

    # dataframe from test and test evaluation
    results_test_df = pd.DataFrame(results_list_test)
    results_test_df.set_index("Model", inplace=True)

    results_train_df = pd.DataFrame(results_list_train)
    results_train_df.set_index("Model", inplace=True)

    # dataframe evaluations diff
    results_diff_df = pd.DataFrame(results_list_diff)
    results_diff_df.set_index("Model", inplace=True)

    # dataframe evaluation diff pct
    results_diff_pct_df = pd.DataFrame(results_list_diff_pct)
    results_diff_pct_df.set_index("Model", inplace=True)

    # new df
    evaluation_df = pd.DataFrame(index=[type(model).__name__ for model in models])
    evaluation_df['CV Accuracy'] = results_test_df['CV Accuracy']
    evaluation_df['Accuracy_Train'] = results_train_df['Accuracy']
    evaluation_df['Accuracy_Test'] = results_test_df['Accuracy']
    evaluation_df['Diff'] = results_diff_df['Accuracy_Difference']
    evaluation_df['Diff (%)'] = results_diff_pct_df['Accuracy_Difference']

    return evaluation_df
In [ ]:
def model_tuning_precision(X_train, y_train, X_test, y_test, params_list, model_type='svc'):
    results_list = []

    for idx, params in enumerate(params_list):
        param_name = f"param_{idx+1}"

        if model_type.lower() == 'svc':
            model = SVC(**params, probability=True, random_state=42)
        elif model_type.lower() == 'xgbclassifier':
            model = XGBClassifier(**params, max_depth=1, random_state=42)
        elif model_type.lower() == 'adaboost':
            model = AdaBoostClassifier(**params, random_state=42)
        else:
            raise ValueError("Invalid model type. Supported types are 'svc', 'xgbclassifier', and 'adaboost'.")

        # Cross-validation
        cv_scores = cross_val_score(model, X_train, y_train, cv=5, scoring='precision')
        avg_cv_precision = cv_scores.mean()

        model.fit(X_train, y_train)

        # train set evaluation
        y_pred_train = model.predict(X_train)
        precision_train = precision_score(y_train, y_pred_train, average='weighted')

        # test set evaluation
        y_pred_test = model.predict(X_test)
        precision_test = precision_score(y_test, y_pred_test, average='weighted')

        results = {
            "Parameter Set": param_name,
            "Model": type(model).__name__,
            "CV Precision": avg_cv_precision,
            "Precision_Train": precision_train,
            "Precision_Test": precision_test,
            "Diff": precision_train - precision_test,
            "Diff (%)": ((precision_train - precision_test) / precision_train) * 100,
            "Parameters": str(params)
        }
        results_list.append(results)

    # Create DataFrame from results list
    evaluation_df = pd.DataFrame(results_list)
    evaluation_df.set_index("Parameter Set", inplace=True)

    return evaluation_df
In [ ]:
def model_tuning_accuracy(X_train, y_train, X_test, y_test, params_list, model_type='svc'):
    results_list = []

    for idx, params in enumerate(params_list):
        param_name = f"param_{idx+1}"

        if model_type.lower() == 'svc':
            model = SVC(**params, probability=True, random_state=42)
        elif model_type.lower() == 'xgbclassifier':
            model = XGBClassifier(**params, max_depth=1, random_state=42)
        elif model_type.lower() == 'adaboost':
            model = AdaBoostClassifier(**params, random_state=42)
        else:
            raise ValueError("Invalid model type. Supported types are 'svc', 'xgbclassifier', and 'adaboost'.")

        # Cross-validation
        cv_scores = cross_val_score(model, X_train, y_train, cv=5, scoring='accuracy')
        avg_cv_accuracy = cv_scores.mean()

        model.fit(X_train, y_train)

        # train set evaluation
        y_pred_train = model.predict(X_train)
        accuracy_train = accuracy_score(y_train, y_pred_train)

        # test set evaluation
        y_pred_test = model.predict(X_test)
        accuracy_test = accuracy_score(y_test, y_pred_test)

        results = {
            "Parameter Set": param_name,
            "Model": type(model).__name__,
            "CV Accuracy": avg_cv_accuracy,
            "Accuracy_Train": accuracy_train,
            "Accuracy_Test": accuracy_test,
            "Diff": accuracy_train - accuracy_test,
            "Diff (%)": ((accuracy_train - accuracy_test) / accuracy_train) * 100,
            "Parameters": str(params)
        }
        results_list.append(results)

    # Create DataFrame from results list
    evaluation_df = pd.DataFrame(results_list)
    evaluation_df.set_index("Parameter Set", inplace=True)

    return evaluation_df

Default Parameter¶

In [ ]:
# first metric (precision)
default_precision = models_precision(X_train, y_train, X_test, y_test)
default_precision
Out[ ]:
CV Precision Precision_Train Precision_Test Diff Diff (%)
SVC 0.799378 0.856226 0.836876 0.019350 2.259904
XGBClassifier 0.847233 0.864785 0.827637 0.037148 4.295622
AdaBoostClassifier 0.872463 0.879967 0.822993 0.056974 6.474576
In [ ]:
# second metric (accuracy)
default_accuracy = models_accuracy(X_train, y_train, X_test, y_test)
default_accuracy
Out[ ]:
CV Accuracy Accuracy_Train Accuracy_Test Diff Diff (%)
SVC 0.832502 0.852899 0.743119 0.109780 12.871371
XGBClassifier 0.840023 0.864710 0.807339 0.057371 6.634668
AdaBoostClassifier 0.861142 0.879742 0.816514 0.063229 7.187166

Hyperparameter Tuning¶

Precision¶

SVM¶

In [ ]:
# precision tuning
# define model and parameters
model = SVC(probability=True)
kernel = ['poly', 'rbf', 'linear']
C = [1, 2, 3, 4, 5]
gamma = ['scale', 'auto']

# define grid search
grid = dict(kernel=kernel,C=C,gamma=gamma)
grid_search = GridSearchCV(estimator=model, param_grid=grid, n_jobs=-1, cv=5, scoring='precision', error_score=0, return_train_score=True)
grid_result = grid_search.fit(X_train, y_train)

# summarize results
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
means_test = grid_result.cv_results_['mean_test_score']
means_train = grid_result.cv_results_['mean_train_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']
for mean_test, mean_train, stdev, param in zip(means_test, means_train, stds, params):
    print(f"{mean_test}, {mean_train}, {stdev} with: {param}")
Best: 0.874959 using {'C': 3, 'gamma': 'auto', 'kernel': 'poly'}
0.8644953563104041, 0.8929407815319182, 0.01856815437095068 with: {'C': 1, 'gamma': 'scale', 'kernel': 'poly'}
0.7993784604063702, 0.8210307612871779, 0.012063588854681057 with: {'C': 1, 'gamma': 'scale', 'kernel': 'rbf'}
0.7922502890434228, 0.793926676438712, 0.003878999368544625 with: {'C': 1, 'gamma': 'scale', 'kernel': 'linear'}
0.8648059951888861, 0.892933514050925, 0.017988902787741075 with: {'C': 1, 'gamma': 'auto', 'kernel': 'poly'}
0.8008227825365651, 0.8226624910842218, 0.01080446522967414 with: {'C': 1, 'gamma': 'auto', 'kernel': 'rbf'}
0.7922502890434228, 0.793926676438712, 0.003878999368544625 with: {'C': 1, 'gamma': 'auto', 'kernel': 'linear'}
0.8697615356892001, 0.8999971848486954, 0.02252521870767744 with: {'C': 2, 'gamma': 'scale', 'kernel': 'poly'}
0.818454516247957, 0.844862851865334, 0.010662542618646867 with: {'C': 2, 'gamma': 'scale', 'kernel': 'rbf'}
0.7908714261187912, 0.7949764167038232, 0.00413311469160671 with: {'C': 2, 'gamma': 'scale', 'kernel': 'linear'}
0.8737489307337661, 0.9026723433894415, 0.025167587428961368 with: {'C': 2, 'gamma': 'auto', 'kernel': 'poly'}
0.8221844859344621, 0.8460160592299794, 0.013148893449438722 with: {'C': 2, 'gamma': 'auto', 'kernel': 'rbf'}
0.7908714261187912, 0.7949764167038232, 0.00413311469160671 with: {'C': 2, 'gamma': 'auto', 'kernel': 'linear'}
0.8745567055442045, 0.9058455806482624, 0.021897984490693974 with: {'C': 3, 'gamma': 'scale', 'kernel': 'poly'}
0.8294839492880092, 0.8574063148341029, 0.013612934368169327 with: {'C': 3, 'gamma': 'scale', 'kernel': 'rbf'}
0.7905774249647815, 0.7943927795369439, 0.00341690076545919 with: {'C': 3, 'gamma': 'scale', 'kernel': 'linear'}
0.8749594226083375, 0.9088298610927522, 0.02410831557897364 with: {'C': 3, 'gamma': 'auto', 'kernel': 'poly'}
0.8304118173766311, 0.8595460877422555, 0.015466872742696048 with: {'C': 3, 'gamma': 'auto', 'kernel': 'rbf'}
0.7905774249647815, 0.7943927795369439, 0.00341690076545919 with: {'C': 3, 'gamma': 'auto', 'kernel': 'linear'}
0.8719363631409017, 0.90922576634777, 0.024107700968200516 with: {'C': 4, 'gamma': 'scale', 'kernel': 'poly'}
0.8363044171567899, 0.867403054422895, 0.014970522763672054 with: {'C': 4, 'gamma': 'scale', 'kernel': 'rbf'}
0.7899151733091522, 0.795143331065292, 0.0040912185497245625 with: {'C': 4, 'gamma': 'scale', 'kernel': 'linear'}
0.8715389911950513, 0.9104298741100247, 0.02191057611185297 with: {'C': 4, 'gamma': 'auto', 'kernel': 'poly'}
0.8384168740408899, 0.8698470491527861, 0.01785773479953582 with: {'C': 4, 'gamma': 'auto', 'kernel': 'rbf'}
0.7899151733091522, 0.795143331065292, 0.0040912185497245625 with: {'C': 4, 'gamma': 'auto', 'kernel': 'linear'}
0.8707849019506877, 0.9109999360143208, 0.020548024900351028 with: {'C': 5, 'gamma': 'scale', 'kernel': 'poly'}
0.8402555911334215, 0.8724592736138066, 0.016895992603648068 with: {'C': 5, 'gamma': 'scale', 'kernel': 'rbf'}
0.7910988656371538, 0.79503972135662, 0.004456659072693344 with: {'C': 5, 'gamma': 'scale', 'kernel': 'linear'}
0.8718026094639788, 0.9132210382199716, 0.02051176454686488 with: {'C': 5, 'gamma': 'auto', 'kernel': 'poly'}
0.843024860734786, 0.8747285893185079, 0.01649835504382095 with: {'C': 5, 'gamma': 'auto', 'kernel': 'rbf'}
0.7910988656371538, 0.79503972135662, 0.004456659072693344 with: {'C': 5, 'gamma': 'auto', 'kernel': 'linear'}
In [ ]:
# evaluation results - precision
svc_tuning_precision = model_tuning_precision(X_train, y_train, X_test, y_test, params, model_type='svc')
svc_tuning_precision
Out[ ]:
Model CV Precision Precision_Train Precision_Test Diff Diff (%) Parameters
Parameter Set
param_1 SVC 0.864495 0.814202 0.804183 0.010019 1.230524 {'C': 1, 'gamma': 'scale', 'kernel': 'poly'}
param_2 SVC 0.799378 0.856226 0.836876 0.019350 2.259904 {'C': 1, 'gamma': 'scale', 'kernel': 'rbf'}
param_3 SVC 0.792250 0.807795 0.846378 -0.038583 -4.776376 {'C': 1, 'gamma': 'scale', 'kernel': 'linear'}
param_4 SVC 0.864806 0.817847 0.804183 0.013664 1.670685 {'C': 1, 'gamma': 'auto', 'kernel': 'poly'}
param_5 SVC 0.800823 0.857977 0.833500 0.024477 2.852910 {'C': 1, 'gamma': 'auto', 'kernel': 'rbf'}
param_6 SVC 0.792250 0.807795 0.846378 -0.038583 -4.776376 {'C': 1, 'gamma': 'auto', 'kernel': 'linear'}
param_7 SVC 0.869762 0.832848 0.811858 0.020990 2.520239 {'C': 2, 'gamma': 'scale', 'kernel': 'poly'}
param_8 SVC 0.818455 0.876420 0.821935 0.054485 6.216769 {'C': 2, 'gamma': 'scale', 'kernel': 'rbf'}
param_9 SVC 0.790871 0.807753 0.847020 -0.039267 -4.861221 {'C': 2, 'gamma': 'scale', 'kernel': 'linear'}
param_10 SVC 0.873749 0.837537 0.814110 0.023427 2.797151 {'C': 2, 'gamma': 'auto', 'kernel': 'poly'}
param_11 SVC 0.822184 0.878579 0.825903 0.052676 5.995626 {'C': 2, 'gamma': 'auto', 'kernel': 'rbf'}
param_12 SVC 0.790871 0.807753 0.847020 -0.039267 -4.861221 {'C': 2, 'gamma': 'auto', 'kernel': 'linear'}
param_13 SVC 0.874557 0.840083 0.810913 0.029170 3.472247 {'C': 3, 'gamma': 'scale', 'kernel': 'poly'}
param_14 SVC 0.829484 0.888494 0.825154 0.063340 7.128920 {'C': 3, 'gamma': 'scale', 'kernel': 'rbf'}
param_15 SVC 0.790577 0.808051 0.846378 -0.038327 -4.743093 {'C': 3, 'gamma': 'scale', 'kernel': 'linear'}
param_16 SVC 0.874959 0.844516 0.807559 0.036957 4.376101 {'C': 3, 'gamma': 'auto', 'kernel': 'poly'}
param_17 SVC 0.830412 0.888974 0.820530 0.068444 7.699262 {'C': 3, 'gamma': 'auto', 'kernel': 'rbf'}
param_18 SVC 0.790577 0.808051 0.846378 -0.038327 -4.743093 {'C': 3, 'gamma': 'auto', 'kernel': 'linear'}
param_19 SVC 0.871936 0.847879 0.810913 0.036966 4.359848 {'C': 4, 'gamma': 'scale', 'kernel': 'poly'}
param_20 SVC 0.836304 0.896172 0.825212 0.070960 7.918164 {'C': 4, 'gamma': 'scale', 'kernel': 'rbf'}
param_21 SVC 0.789915 0.807673 0.846378 -0.038705 -4.792119 {'C': 4, 'gamma': 'scale', 'kernel': 'linear'}
param_22 SVC 0.871539 0.852063 0.806533 0.045530 5.343496 {'C': 4, 'gamma': 'auto', 'kernel': 'poly'}
param_23 SVC 0.838417 0.895851 0.821919 0.073932 8.252699 {'C': 4, 'gamma': 'auto', 'kernel': 'rbf'}
param_24 SVC 0.789915 0.807673 0.846378 -0.038705 -4.792119 {'C': 4, 'gamma': 'auto', 'kernel': 'linear'}
param_25 SVC 0.870785 0.853597 0.809886 0.043711 5.120801 {'C': 5, 'gamma': 'scale', 'kernel': 'poly'}
param_26 SVC 0.840256 0.898973 0.822615 0.076358 8.493901 {'C': 5, 'gamma': 'scale', 'kernel': 'rbf'}
param_27 SVC 0.791099 0.808051 0.846378 -0.038327 -4.743093 {'C': 5, 'gamma': 'scale', 'kernel': 'linear'}
param_28 SVC 0.871803 0.857475 0.816533 0.040941 4.774655 {'C': 5, 'gamma': 'auto', 'kernel': 'poly'}
param_29 SVC 0.843025 0.901236 0.822615 0.078621 8.723685 {'C': 5, 'gamma': 'auto', 'kernel': 'rbf'}
param_30 SVC 0.791099 0.808051 0.846378 -0.038327 -4.743093 {'C': 5, 'gamma': 'auto', 'kernel': 'linear'}
In [ ]:
# filter parameter - precision
filter = (svc_tuning_precision['CV Precision'] > 0.8) & (svc_tuning_precision['Diff'] > 0)
svc_tuning_precision[filter].sort_values(by=['CV Precision','Diff'], ascending=[False,True]).nlargest(3, columns=['CV Precision'])
Out[ ]:
Model CV Precision Precision_Train Precision_Test Diff Diff (%) Parameters
Parameter Set
param_16 SVC 0.874959 0.844516 0.807559 0.036957 4.376101 {'C': 3, 'gamma': 'auto', 'kernel': 'poly'}
param_13 SVC 0.874557 0.840083 0.810913 0.029170 3.472247 {'C': 3, 'gamma': 'scale', 'kernel': 'poly'}
param_10 SVC 0.873749 0.837537 0.814110 0.023427 2.797151 {'C': 2, 'gamma': 'auto', 'kernel': 'poly'}
In [ ]:
# get parameter
params_svc_precision = svc_tuning_precision.loc['param_10']
params_svc_precision['Parameters']
Out[ ]:
"{'C': 2, 'gamma': 'auto', 'kernel': 'poly'}"

XGB Classifier¶

In [ ]:
# Define XGBClassifier model and parameters
model = XGBClassifier(max_depth=1)
objective = ['reg:squarederror','binary:logistic','reg:logistic']
learning_rate = [0.3, 0.5, 0.8, 1.0]
n_estimators = [50, 100, 150, 200]
subsample = [0.8, 0.9, 1.0]
colsample_bytree = [0.8, 0.9, 1.0]
gamma = [1,2,3]

# Define grid search
grid = dict(objective=objective, learning_rate=learning_rate, n_estimators=n_estimators, subsample=subsample, colsample_bytree=colsample_bytree, gamma=gamma)
grid_search = GridSearchCV(estimator=model, param_grid=grid, n_jobs=-1, cv=5, scoring='precision', error_score=0, return_train_score=True)
grid_result = grid_search.fit(X_train, y_train)

# Summarize results
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
means_test = grid_result.cv_results_['mean_test_score']
means_train = grid_result.cv_results_['mean_train_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']

for mean_test, mean_train, stdev, param in zip(means_test, means_train, stds, params):
    print(f"{mean_test}, {mean_train}, {stdev} with: {param}")
Best: 0.910658 using {'colsample_bytree': 1.0, 'gamma': 2, 'learning_rate': 0.8, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8299457744641299, 0.8389208943404004, 0.014888192993427939 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8293090267767853, 0.8398972118929592, 0.008221253825128958 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8293335554208031, 0.8373391538639574, 0.010104068645862965 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8228856680385596, 0.8342904887499492, 0.01706061828633937 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8216838405322011, 0.8355145170747902, 0.016266046642534795 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8189310282017626, 0.8326457004936584, 0.01001571499292632 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8228856680385596, 0.8342904887499492, 0.01706061828633937 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8216838405322011, 0.8355145170747902, 0.016266046642534795 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8189310282017626, 0.8326457004936584, 0.01001571499292632 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8548548879714687, 0.8700321016290957, 0.009469492081913793 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8534811698500115, 0.8674051385767629, 0.014341680290427889 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8466588430991904, 0.8612556131114321, 0.012060413520002597 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8471510688632365, 0.8677982753797234, 0.012090710654316427 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 0.8}
0.850585282221234, 0.8662002276131936, 0.01307394721397286 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8487908939834, 0.8656186629733265, 0.0150989689456641 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8471510688632365, 0.8677982753797234, 0.012090710654316427 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 0.8}
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0.8652454751934405, 0.8853199055866707, 0.013597533095986538 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8539982343537448, 0.8838708233030822, 0.018564797137560506 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8553582758357308, 0.8880444318792845, 0.0168704869361817 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8652454751934405, 0.8853199055866707, 0.013597533095986538 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8268347479293168, 0.8508421058482257, 0.012019598518685585 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8354998209838748, 0.8528828058637952, 0.020347331816037224 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8211156286868011, 0.8402909247527685, 0.021244409097678878 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8891712237707635, 0.9147744939568966, 0.015350457700413532 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 0.8}
0.883682760430475, 0.9126012336037693, 0.01747924511200719 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8743120542492617, 0.904831723173008, 0.014133963628425063 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8891712237707635, 0.9147744939568966, 0.015350457700413532 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 0.8}
0.883682760430475, 0.9126012336037693, 0.01747924511200719 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8743120542492617, 0.904831723173008, 0.014133963628425063 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8312050792792265, 0.8523389556485007, 0.011185650187202596 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8358207360045856, 0.854031668930815, 0.0201491439036728 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8211156286868011, 0.8402909247527685, 0.021244409097678878 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.9051841264872778, 0.9265349383092293, 0.013345511879383223 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8905346758196593, 0.9215339504988457, 0.013838531755500831 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.9}
0.874877430901595, 0.9044065081956905, 0.014722430735983807 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 1.0}
0.9051841264872778, 0.9265349383092293, 0.013345511879383223 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8905346758196593, 0.9215339504988457, 0.013838531755500831 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 0.9}
0.874877430901595, 0.9044065081956905, 0.014722430735983807 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8271171348083242, 0.8493471195411813, 0.007070903395014026 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8319741380660467, 0.8519953448383228, 0.016339204151552388 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8211156286868011, 0.8402909247527685, 0.021244409097678878 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.901321593552305, 0.9256612211809445, 0.015169276961400677 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8925073144281681, 0.9212773973131603, 0.013573054364919413 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.9}
0.874877430901595, 0.9044065081956905, 0.014722430735983807 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 1.0}
0.901321593552305, 0.9256612211809445, 0.015169276961400677 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8925073144281681, 0.9212773973131603, 0.013573054364919413 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 0.9}
0.874877430901595, 0.9044065081956905, 0.014722430735983807 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 0.8, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8485935359639768, 0.8609769462491654, 0.026480437078480447 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8368696557137548, 0.8496959169208035, 0.023913133610723274 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8340819434564676, 0.8523541036437466, 0.0184633131148186 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8674866954277691, 0.8909117791851566, 0.023479268189447875 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8704899764230987, 0.8934099383586146, 0.02015526920293098 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8713752637036626, 0.8971574760371489, 0.018151263863591484 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8674866954277691, 0.8909117791851566, 0.023479268189447875 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8704899764230987, 0.8934099383586146, 0.02015526920293098 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8713752637036626, 0.8971574760371489, 0.018151263863591484 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8513440184776678, 0.8648843167262925, 0.022485952156250013 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8396716620260343, 0.8510561025614963, 0.02501487961734432 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8340819434564676, 0.8523541036437466, 0.0184633131148186 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8919297721443067, 0.9202742628147863, 0.019338913037478495 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8955218953129384, 0.9218254898140321, 0.014722852489633849 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8788701478318526, 0.9077477388651344, 0.02247169490147497 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8919297721443067, 0.9202742628147863, 0.019338913037478495 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8955218953129384, 0.9218254898140321, 0.014722852489633849 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8788701478318526, 0.9077477388651344, 0.02247169490147497 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8460593334096789, 0.8625128965624752, 0.013377831002613633 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8430671632788181, 0.8576221300175668, 0.023228210809970787 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8340819434564676, 0.8523541036437466, 0.0184633131148186 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.9101360197593298, 0.9346848048357088, 0.015228605259143572 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8978595310468606, 0.9287365369386084, 0.017626192378520717 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8788701478318526, 0.9077477388651344, 0.02247169490147497 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 1.0}
0.9101360197593298, 0.9346848048357088, 0.015228605259143572 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8978595310468606, 0.9287365369386084, 0.017626192378520717 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8788701478318526, 0.9077477388651344, 0.02247169490147497 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8409334140223935, 0.8575324028723953, 0.017228771524378086 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8411308810218898, 0.849178146569405, 0.024250055654701567 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8340819434564676, 0.8523541036437466, 0.0184633131148186 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.9076538543403861, 0.9390165047775716, 0.017881679646635718 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8974378783432557, 0.930424879822716, 0.01594900940566984 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8788701478318526, 0.9077477388651344, 0.02247169490147497 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 1.0}
0.9076538543403861, 0.9390165047775716, 0.017881679646635718 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8974378783432557, 0.930424879822716, 0.01594900940566984 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8788701478318526, 0.9077477388651344, 0.02247169490147497 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 1.0}
In [ ]:
xgb_tuning_precision = model_tuning_precision(X_train, y_train, X_test, y_test, params, model_type='xgbclassifier')
xgb_tuning_precision
Out[ ]:
Model CV Precision Precision_Train Precision_Test Diff Diff (%) Parameters
Parameter Set
param_1 XGBClassifier 0.825169 0.850661 0.835631 0.015030 1.766848 {'colsample_bytree': 0.8, 'gamma': 1, 'learnin...
param_2 XGBClassifier 0.828413 0.845522 0.833142 0.012380 1.464172 {'colsample_bytree': 0.8, 'gamma': 1, 'learnin...
param_3 XGBClassifier 0.826268 0.840944 0.827472 0.013472 1.601998 {'colsample_bytree': 0.8, 'gamma': 1, 'learnin...
param_4 XGBClassifier 0.820744 0.837713 0.827491 0.010222 1.220218 {'colsample_bytree': 0.8, 'gamma': 1, 'learnin...
param_5 XGBClassifier 0.816850 0.836493 0.829966 0.006527 0.780327 {'colsample_bytree': 0.8, 'gamma': 1, 'learnin...
... ... ... ... ... ... ... ...
param_1292 XGBClassifier 0.900328 0.915380 0.840051 0.075329 8.229262 {'colsample_bytree': 1.0, 'gamma': 3, 'learnin...
param_1293 XGBClassifier 0.878870 0.895441 0.823811 0.071630 7.999425 {'colsample_bytree': 1.0, 'gamma': 3, 'learnin...
param_1294 XGBClassifier 0.905373 0.922569 0.819691 0.102878 11.151284 {'colsample_bytree': 1.0, 'gamma': 3, 'learnin...
param_1295 XGBClassifier 0.900328 0.915380 0.840051 0.075329 8.229262 {'colsample_bytree': 1.0, 'gamma': 3, 'learnin...
param_1296 XGBClassifier 0.878870 0.895441 0.823811 0.071630 7.999425 {'colsample_bytree': 1.0, 'gamma': 3, 'learnin...

1296 rows × 7 columns

In [ ]:
# filter parameter - precision
filter = (xgb_tuning_precision['CV Precision'] > 0.8) & (xgb_tuning_precision['Diff'] > 0)
xgb_tuning_precision[filter].sort_values(by=['CV Precision','Diff'], ascending=[False,True]).nlargest(3, columns=['CV Precision'])
Out[ ]:
Model CV Precision Precision_Train Precision_Test Diff Diff (%) Parameters
Parameter Set
param_140 XGBClassifier 0.912467 0.922357 0.818055 0.104302 11.308190 {'colsample_bytree': 0.8, 'gamma': 1, 'learnin...
param_143 XGBClassifier 0.912467 0.922357 0.818055 0.104302 11.308190 {'colsample_bytree': 0.8, 'gamma': 1, 'learnin...
param_1147 XGBClassifier 0.912229 0.924031 0.835093 0.088938 9.625034 {'colsample_bytree': 1.0, 'gamma': 2, 'learnin...
In [ ]:
# get parameter
params_xgb_precision = xgb_tuning_precision.loc['param_140']
params_xgb_precision['Parameters']
Out[ ]:
"{'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.9}"
In [ ]:
# Initialize XGBoost model
params = {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.9}
xgb_model = XGBClassifier(**params, max_depth=1, random_state=42)

# Perform cross-validation with precision as the scoring metric
precision_scores = cross_val_score(xgb_model, X_train, y_train, cv=5, scoring='precision')

# Print mean and standard deviation of precision scores
print("Mean Precision:", np.mean(precision_scores))
print("Standard Deviation of Precision:", np.std(precision_scores))

# Fit the model on the entire training data
xgb_model.fit(X_train, y_train)

# Feature importances
importance_xgb = pd.Series(xgb_model.feature_importances_, index=X_train.columns).sort_values(ascending=True)

# Plotting (using barh for horizontal bar plot)
ax = importance_xgb.plot.barh(align="center", color='#e9c369')
ax.set_title("XGB Classifier Feature Importances")
ax.figure.tight_layout()
plt.show()
/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
  and should_run_async(code)
Mean Precision: 0.9124665259093071
Standard Deviation of Precision: 0.016099837171287733

AdaBoost Classifier¶

In [ ]:
# Define AdaBoostClassifier model and parameters
model = AdaBoostClassifier(random_state=42)
learning_rate = [0.01, 0.1, 0.2, 0.3, 0.5, 0.8, 1.0]
n_estimators = [50, 100, 150, 200]
algorithm = ['SAMME', 'SAMME.R']

# Define grid search
grid = dict(learning_rate=learning_rate, n_estimators=n_estimators, algorithm=algorithm)
grid_search = GridSearchCV(estimator=model, param_grid=grid, n_jobs=-1, cv=5, scoring='precision', error_score=0, return_train_score=True)
grid_result = grid_search.fit(X_train, y_train)

# Summarize results
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
means_test = grid_result.cv_results_['mean_test_score']
means_train = grid_result.cv_results_['mean_train_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']

for mean_test, mean_train, stdev, param in zip(means_test, means_train, stds, params):
    print(f"{mean_test}, {mean_train}, {stdev} with: {param}")
Best: 0.908271 using {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 200}
0.6492129025796463, 0.6483929230586379, 0.016845244587131936 with: {'algorithm': 'SAMME', 'learning_rate': 0.01, 'n_estimators': 50}
0.6481665781698287, 0.6536249086884554, 0.015391772893054802 with: {'algorithm': 'SAMME', 'learning_rate': 0.01, 'n_estimators': 100}
0.6978192522770351, 0.7011184776316066, 0.023461272691963353 with: {'algorithm': 'SAMME', 'learning_rate': 0.01, 'n_estimators': 150}
0.7143417916689243, 0.7218193697536923, 0.02187046474265906 with: {'algorithm': 'SAMME', 'learning_rate': 0.01, 'n_estimators': 200}
0.7741031546092639, 0.7910114003195097, 0.020136252906739694 with: {'algorithm': 'SAMME', 'learning_rate': 0.1, 'n_estimators': 50}
0.7920923098178263, 0.808250360899665, 0.022840059522987156 with: {'algorithm': 'SAMME', 'learning_rate': 0.1, 'n_estimators': 100}
0.7897202908849246, 0.8101847186221949, 0.019488280391017073 with: {'algorithm': 'SAMME', 'learning_rate': 0.1, 'n_estimators': 150}
0.7981894861638571, 0.8122734741370502, 0.017207304914300448 with: {'algorithm': 'SAMME', 'learning_rate': 0.1, 'n_estimators': 200}
0.7922655606464397, 0.8067944593762009, 0.026223375631359194 with: {'algorithm': 'SAMME', 'learning_rate': 0.2, 'n_estimators': 50}
0.7926709666042697, 0.8075427745566068, 0.01956882844555728 with: {'algorithm': 'SAMME', 'learning_rate': 0.2, 'n_estimators': 100}
0.8053032028562879, 0.8193491631051957, 0.02114187021855238 with: {'algorithm': 'SAMME', 'learning_rate': 0.2, 'n_estimators': 150}
0.8088078920584441, 0.8261938141175433, 0.022245577041637588 with: {'algorithm': 'SAMME', 'learning_rate': 0.2, 'n_estimators': 200}
0.8027496143255508, 0.8162847569153773, 0.014800250522407958 with: {'algorithm': 'SAMME', 'learning_rate': 0.3, 'n_estimators': 50}
0.8115298990560648, 0.8218829459586697, 0.02169416571727745 with: {'algorithm': 'SAMME', 'learning_rate': 0.3, 'n_estimators': 100}
0.8235857476444908, 0.8342885774868224, 0.015784528624701036 with: {'algorithm': 'SAMME', 'learning_rate': 0.3, 'n_estimators': 150}
0.8332554594576814, 0.8443920380169654, 0.02022584692235086 with: {'algorithm': 'SAMME', 'learning_rate': 0.3, 'n_estimators': 200}
0.8109491608928519, 0.8256750451810884, 0.016063004696718102 with: {'algorithm': 'SAMME', 'learning_rate': 0.5, 'n_estimators': 50}
0.8383133802442699, 0.8459384336022439, 0.020463931825548558 with: {'algorithm': 'SAMME', 'learning_rate': 0.5, 'n_estimators': 100}
0.8520541924726521, 0.868237492388252, 0.01909883243025282 with: {'algorithm': 'SAMME', 'learning_rate': 0.5, 'n_estimators': 150}
0.8554486452478848, 0.8741264281055463, 0.016249676363641396 with: {'algorithm': 'SAMME', 'learning_rate': 0.5, 'n_estimators': 200}
0.8385508869975627, 0.8510792469833289, 0.01507930171908834 with: {'algorithm': 'SAMME', 'learning_rate': 0.8, 'n_estimators': 50}
0.8608792877132567, 0.8861656732332917, 0.019810730521170997 with: {'algorithm': 'SAMME', 'learning_rate': 0.8, 'n_estimators': 100}
0.8663340131964483, 0.8880402808701776, 0.015449005182397132 with: {'algorithm': 'SAMME', 'learning_rate': 0.8, 'n_estimators': 150}
0.8780231974140461, 0.8941777917310516, 0.01620907660722986 with: {'algorithm': 'SAMME', 'learning_rate': 0.8, 'n_estimators': 200}
0.8492118876912322, 0.8684037807169277, 0.017496843397156774 with: {'algorithm': 'SAMME', 'learning_rate': 1.0, 'n_estimators': 50}
0.8632976683046565, 0.8880285672993706, 0.01868966734811718 with: {'algorithm': 'SAMME', 'learning_rate': 1.0, 'n_estimators': 100}
0.8821573122055067, 0.9032788502681613, 0.017446611247299147 with: {'algorithm': 'SAMME', 'learning_rate': 1.0, 'n_estimators': 150}
0.8827708932514792, 0.9072559009234059, 0.015755686953888043 with: {'algorithm': 'SAMME', 'learning_rate': 1.0, 'n_estimators': 200}
0.6490939587056719, 0.6534428171807594, 0.014436633770513825 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.01, 'n_estimators': 50}
0.7099828256270964, 0.7174151620720879, 0.015419428150022596 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.01, 'n_estimators': 100}
0.7229098710982346, 0.7319493649714768, 0.016061284278344252 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.01, 'n_estimators': 150}
0.7423688566704706, 0.752377811059427, 0.019765496022307358 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.01, 'n_estimators': 200}
0.7852388342132902, 0.7950171406858104, 0.016860003031678637 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.1, 'n_estimators': 50}
0.8017634695936557, 0.8132264625833647, 0.009684776139796762 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.1, 'n_estimators': 100}
0.8183604486250415, 0.8312745791243936, 0.010758529494339204 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.1, 'n_estimators': 150}
0.8296336991140434, 0.8440187844352256, 0.013945072807370496 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.1, 'n_estimators': 200}
0.7995899610490653, 0.813977564473379, 0.007451251330903823 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.2, 'n_estimators': 50}
0.833774227844047, 0.8464073695128302, 0.016464257032295194 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.2, 'n_estimators': 100}
0.8492455343764036, 0.8632262232467707, 0.014447931352461764 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.2, 'n_estimators': 150}
0.8582696093958649, 0.8786438595274436, 0.016366243283525 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.2, 'n_estimators': 200}
0.8224710072356645, 0.835541500387432, 0.010516732917087757 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.3, 'n_estimators': 50}
0.84998718027335, 0.8684274308355654, 0.014484054528523504 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.3, 'n_estimators': 100}
0.8664717361345332, 0.8882350871717314, 0.01875774503066364 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.3, 'n_estimators': 150}
0.8764391092845185, 0.8979913560745288, 0.01639436499593488 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.3, 'n_estimators': 200}
0.8428472052663828, 0.863361847485766, 0.017747013628598252 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.5, 'n_estimators': 50}
0.8706493507552402, 0.89498604155281, 0.012884893159994764 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.5, 'n_estimators': 100}
0.8862269073301036, 0.9081727144945514, 0.014684080395021903 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.5, 'n_estimators': 150}
0.8955467113070703, 0.915046486262384, 0.013327170691222683 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.5, 'n_estimators': 200}
0.8605723896741674, 0.885645353691421, 0.017953642566500748 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.8, 'n_estimators': 50}
0.8911474857423715, 0.9105714212160256, 0.01755219172638399 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.8, 'n_estimators': 100}
0.8989724265285923, 0.9219293146720087, 0.01560168887937801 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.8, 'n_estimators': 150}
0.9001824427320889, 0.9288254631291938, 0.013488662528999029 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.8, 'n_estimators': 200}
0.8724634280697028, 0.8948320894885159, 0.015675246369269036 with: {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 50}
0.8929582286758455, 0.9172776025228355, 0.018418534128540092 with: {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 100}
0.9032525488063451, 0.9242118947069929, 0.019295069138344296 with: {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 150}
0.9082709380712878, 0.9358567904100219, 0.02648982805265192 with: {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 200}
In [ ]:
adb_tuning_precision = model_tuning_precision(X_train, y_train, X_test, y_test, params, model_type='adaboost')
adb_tuning_precision
Out[ ]:
Model CV Precision Precision_Train Precision_Test Diff Diff (%) Parameters
Parameter Set
param_1 AdaBoostClassifier 0.649213 0.711296 0.802507 -0.091211 -12.823232 {'algorithm': 'SAMME', 'learning_rate': 0.01, ...
param_2 AdaBoostClassifier 0.648167 0.711701 0.790427 -0.078726 -11.061692 {'algorithm': 'SAMME', 'learning_rate': 0.01, ...
param_3 AdaBoostClassifier 0.697819 0.736949 0.797057 -0.060109 -8.156405 {'algorithm': 'SAMME', 'learning_rate': 0.01, ...
param_4 AdaBoostClassifier 0.714342 0.737002 0.793539 -0.056537 -7.671175 {'algorithm': 'SAMME', 'learning_rate': 0.01, ...
param_5 AdaBoostClassifier 0.774103 0.781595 0.807536 -0.025941 -3.318927 {'algorithm': 'SAMME', 'learning_rate': 0.1, '...
param_6 AdaBoostClassifier 0.792092 0.802792 0.810650 -0.007859 -0.978912 {'algorithm': 'SAMME', 'learning_rate': 0.1, '...
param_7 AdaBoostClassifier 0.789720 0.820168 0.817211 0.002957 0.360545 {'algorithm': 'SAMME', 'learning_rate': 0.1, '...
param_8 AdaBoostClassifier 0.798189 0.825283 0.823851 0.001432 0.173518 {'algorithm': 'SAMME', 'learning_rate': 0.1, '...
param_9 AdaBoostClassifier 0.792266 0.810844 0.812586 -0.001742 -0.214828 {'algorithm': 'SAMME', 'learning_rate': 0.2, '...
param_10 AdaBoostClassifier 0.792671 0.816348 0.824742 -0.008394 -1.028224 {'algorithm': 'SAMME', 'learning_rate': 0.2, '...
param_11 AdaBoostClassifier 0.805303 0.835488 0.832016 0.003472 0.415603 {'algorithm': 'SAMME', 'learning_rate': 0.2, '...
param_12 AdaBoostClassifier 0.808808 0.842031 0.828284 0.013747 1.632651 {'algorithm': 'SAMME', 'learning_rate': 0.2, '...
param_13 AdaBoostClassifier 0.802750 0.817125 0.816566 0.000559 0.068402 {'algorithm': 'SAMME', 'learning_rate': 0.3, '...
param_14 AdaBoostClassifier 0.811530 0.837601 0.824233 0.013368 1.595944 {'algorithm': 'SAMME', 'learning_rate': 0.3, '...
param_15 AdaBoostClassifier 0.823586 0.847405 0.824233 0.023172 2.734462 {'algorithm': 'SAMME', 'learning_rate': 0.3, '...
param_16 AdaBoostClassifier 0.833255 0.855825 0.823515 0.032309 3.775241 {'algorithm': 'SAMME', 'learning_rate': 0.3, '...
param_17 AdaBoostClassifier 0.810949 0.833202 0.826708 0.006493 0.779341 {'algorithm': 'SAMME', 'learning_rate': 0.5, '...
param_18 AdaBoostClassifier 0.838313 0.857308 0.819657 0.037652 4.391845 {'algorithm': 'SAMME', 'learning_rate': 0.5, '...
param_19 AdaBoostClassifier 0.852054 0.871906 0.815523 0.056383 6.466587 {'algorithm': 'SAMME', 'learning_rate': 0.5, '...
param_20 AdaBoostClassifier 0.855449 0.876164 0.821865 0.054299 6.197341 {'algorithm': 'SAMME', 'learning_rate': 0.5, '...
param_21 AdaBoostClassifier 0.838551 0.855509 0.815478 0.040032 4.679263 {'algorithm': 'SAMME', 'learning_rate': 0.8, '...
param_22 AdaBoostClassifier 0.860879 0.880849 0.810752 0.070097 7.957845 {'algorithm': 'SAMME', 'learning_rate': 0.8, '...
param_23 AdaBoostClassifier 0.866334 0.887425 0.824434 0.062991 7.098191 {'algorithm': 'SAMME', 'learning_rate': 0.8, '...
param_24 AdaBoostClassifier 0.878023 0.891516 0.832926 0.058590 6.571986 {'algorithm': 'SAMME', 'learning_rate': 0.8, '...
param_25 AdaBoostClassifier 0.849212 0.871961 0.819175 0.052785 6.053645 {'algorithm': 'SAMME', 'learning_rate': 1.0, '...
param_26 AdaBoostClassifier 0.863298 0.882214 0.808512 0.073702 8.354178 {'algorithm': 'SAMME', 'learning_rate': 1.0, '...
param_27 AdaBoostClassifier 0.882157 0.887904 0.816892 0.071012 7.997676 {'algorithm': 'SAMME', 'learning_rate': 1.0, '...
param_28 AdaBoostClassifier 0.882771 0.893509 0.828283 0.065227 7.300039 {'algorithm': 'SAMME', 'learning_rate': 1.0, '...
param_29 AdaBoostClassifier 0.649094 0.711996 0.790427 -0.078431 -11.015625 {'algorithm': 'SAMME.R', 'learning_rate': 0.01...
param_30 AdaBoostClassifier 0.709983 0.737859 0.790033 -0.052173 -7.070882 {'algorithm': 'SAMME.R', 'learning_rate': 0.01...
param_31 AdaBoostClassifier 0.722910 0.752078 0.796505 -0.044426 -5.907148 {'algorithm': 'SAMME.R', 'learning_rate': 0.01...
param_32 AdaBoostClassifier 0.742369 0.769917 0.798014 -0.028098 -3.649448 {'algorithm': 'SAMME.R', 'learning_rate': 0.01...
param_33 AdaBoostClassifier 0.785239 0.810770 0.813240 -0.002470 -0.304647 {'algorithm': 'SAMME.R', 'learning_rate': 0.1,...
param_34 AdaBoostClassifier 0.801763 0.818932 0.816745 0.002187 0.267035 {'algorithm': 'SAMME.R', 'learning_rate': 0.1,...
param_35 AdaBoostClassifier 0.818360 0.836725 0.820971 0.015754 1.882816 {'algorithm': 'SAMME.R', 'learning_rate': 0.1,...
param_36 AdaBoostClassifier 0.829634 0.850601 0.823515 0.027086 3.184370 {'algorithm': 'SAMME.R', 'learning_rate': 0.1,...
param_37 AdaBoostClassifier 0.799590 0.823803 0.826708 -0.002906 -0.352699 {'algorithm': 'SAMME.R', 'learning_rate': 0.2,...
param_38 AdaBoostClassifier 0.833774 0.851398 0.822653 0.028745 3.376218 {'algorithm': 'SAMME.R', 'learning_rate': 0.2,...
param_39 AdaBoostClassifier 0.849246 0.866599 0.824764 0.041835 4.827492 {'algorithm': 'SAMME.R', 'learning_rate': 0.2,...
param_40 AdaBoostClassifier 0.858270 0.876163 0.819831 0.056332 6.429434 {'algorithm': 'SAMME.R', 'learning_rate': 0.2,...
param_41 AdaBoostClassifier 0.822471 0.844017 0.828308 0.015709 1.861217 {'algorithm': 'SAMME.R', 'learning_rate': 0.3,...
param_42 AdaBoostClassifier 0.849987 0.862321 0.824764 0.037557 4.355310 {'algorithm': 'SAMME.R', 'learning_rate': 0.3,...
param_43 AdaBoostClassifier 0.866472 0.878339 0.820858 0.057480 6.544208 {'algorithm': 'SAMME.R', 'learning_rate': 0.3,...
param_44 AdaBoostClassifier 0.876439 0.887056 0.826518 0.060538 6.824629 {'algorithm': 'SAMME.R', 'learning_rate': 0.3,...
param_45 AdaBoostClassifier 0.842847 0.862922 0.821503 0.041420 4.799913 {'algorithm': 'SAMME.R', 'learning_rate': 0.5,...
param_46 AdaBoostClassifier 0.870649 0.885182 0.817561 0.067621 7.639203 {'algorithm': 'SAMME.R', 'learning_rate': 0.5,...
param_47 AdaBoostClassifier 0.886227 0.893469 0.823208 0.070261 7.863885 {'algorithm': 'SAMME.R', 'learning_rate': 0.5,...
param_48 AdaBoostClassifier 0.895547 0.898486 0.825688 0.072798 8.102283 {'algorithm': 'SAMME.R', 'learning_rate': 0.5,...
param_49 AdaBoostClassifier 0.860572 0.881286 0.822969 0.058318 6.617339 {'algorithm': 'SAMME.R', 'learning_rate': 0.8,...
param_50 AdaBoostClassifier 0.891147 0.897105 0.809602 0.087503 9.753906 {'algorithm': 'SAMME.R', 'learning_rate': 0.8,...
param_51 AdaBoostClassifier 0.898972 0.906498 0.818208 0.088290 9.739665 {'algorithm': 'SAMME.R', 'learning_rate': 0.8,...
param_52 AdaBoostClassifier 0.900182 0.912634 0.822355 0.090279 9.892101 {'algorithm': 'SAMME.R', 'learning_rate': 0.8,...
param_53 AdaBoostClassifier 0.872463 0.879967 0.822993 0.056974 6.474576 {'algorithm': 'SAMME.R', 'learning_rate': 1.0,...
param_54 AdaBoostClassifier 0.892958 0.901083 0.816345 0.084738 9.403998 {'algorithm': 'SAMME.R', 'learning_rate': 1.0,...
param_55 AdaBoostClassifier 0.903253 0.910859 0.831003 0.079856 8.767125 {'algorithm': 'SAMME.R', 'learning_rate': 1.0,...
param_56 AdaBoostClassifier 0.908271 0.918675 0.843316 0.075359 8.203003 {'algorithm': 'SAMME.R', 'learning_rate': 1.0,...
In [ ]:
# filter parameter - precision
filter = (adb_tuning_precision['CV Precision'] > 0.8) & (adb_tuning_precision['Diff'] > 0)
adb_tuning_precision[filter].sort_values(by=['CV Precision','Diff'], ascending=[False,True]).nlargest(3, columns=['CV Precision'])
Out[ ]:
Model CV Precision Precision_Train Precision_Test Diff Diff (%) Parameters
Parameter Set
param_56 AdaBoostClassifier 0.908271 0.918675 0.843316 0.075359 8.203003 {'algorithm': 'SAMME.R', 'learning_rate': 1.0,...
param_55 AdaBoostClassifier 0.903253 0.910859 0.831003 0.079856 8.767125 {'algorithm': 'SAMME.R', 'learning_rate': 1.0,...
param_52 AdaBoostClassifier 0.900182 0.912634 0.822355 0.090279 9.892101 {'algorithm': 'SAMME.R', 'learning_rate': 0.8,...
In [ ]:
# get parameter
params_adb_precision = adb_tuning_precision.loc['param_56']
params_adb_precision['Parameters']
Out[ ]:
"{'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 200}"
In [ ]:
# Initialize Adaboost with DecisionTreeClassifier as base estimator
params = {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 200}
adaboost_model = AdaBoostClassifier(**params, random_state=42)

# Perform cross-validation with precision as the scoring metric
precision_scores = cross_val_score(adaboost_model, X_train, y_train, cv=5, scoring='precision')

# Print mean and standard deviation of precision scores
print("Mean Precision:", np.mean(precision_scores))
print("Standard Deviation of Precision:", np.std(precision_scores))

# Fit the model on the entire training data
adaboost_model.fit(X_train, y_train)

# Feature importances
importance_adb = pd.Series(adaboost_model.feature_importances_, index=X_train.columns).sort_values(ascending=True)

# Plotting (using barh for horizontal bar plot)
ax = importance_adb.plot.barh(align="center", color='#e9c369')
ax.set_title("Adaboost Feature Importances")
ax.figure.tight_layout()
plt.show()
Mean Precision: 0.9082709380712878
Standard Deviation of Precision: 0.02648982805265192

Accuracy¶

SVM¶

In [ ]:
# accuracy tuning
# define model and parameters
model = SVC(probability=True)
kernel = ['poly', 'rbf', 'linear']
C = [1, 2, 3, 4, 5]
gamma = ['scale', 'auto']

# define grid search
grid = dict(kernel=kernel,C=C,gamma=gamma)
grid_search = GridSearchCV(estimator=model, param_grid=grid, n_jobs=-1, cv=5, scoring='accuracy', error_score=0, return_train_score=True)
grid_result = grid_search.fit(X_train, y_train)

# summarize results
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
means_test = grid_result.cv_results_['mean_test_score']
means_train = grid_result.cv_results_['mean_train_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']
for mean_test, mean_train, stdev, param in zip(means_test, means_train, stds, params):
    print(f"{mean_test}, {mean_train}, {stdev} with: {param}")
Best: 0.872946 using {'C': 5, 'gamma': 'auto', 'kernel': 'rbf'}
0.771293464391739, 0.788385539854246, 0.01600741715998074 with: {'C': 1, 'gamma': 'scale', 'kernel': 'poly'}
0.8325017151723829, 0.851914892765525, 0.01823377390974064 with: {'C': 1, 'gamma': 'scale', 'kernel': 'rbf'}
0.8060117593500941, 0.8078022435397182, 0.007238425945776351 with: {'C': 1, 'gamma': 'scale', 'kernel': 'linear'}
0.777734177134027, 0.794380785439003, 0.013134682520941026 with: {'C': 1, 'gamma': 'auto', 'kernel': 'poly'}
0.8342912651239732, 0.8537044818767935, 0.016771402944234844 with: {'C': 1, 'gamma': 'auto', 'kernel': 'rbf'}
0.8060117593500941, 0.8078022435397182, 0.007238425945776351 with: {'C': 1, 'gamma': 'auto', 'kernel': 'linear'}
0.794916036701483, 0.8137073233202466, 0.010315091279536347 with: {'C': 2, 'gamma': 'scale', 'kernel': 'poly'}
0.8507562788132932, 0.8716895783057794, 0.016993674201332572 with: {'C': 2, 'gamma': 'scale', 'kernel': 'rbf'}
0.8045787087797592, 0.8086970180851873, 0.006991492873876753 with: {'C': 2, 'gamma': 'scale', 'kernel': 'linear'}
0.8009996088765782, 0.817197056104501, 0.01250702581961604 with: {'C': 2, 'gamma': 'auto', 'kernel': 'poly'}
0.8532607510852073, 0.8733001164591613, 0.018802791600030466 with: {'C': 2, 'gamma': 'auto', 'kernel': 'rbf'}
0.8045787087797592, 0.8086970180851873, 0.006991492873876753 with: {'C': 2, 'gamma': 'auto', 'kernel': 'linear'}
0.8042196446547534, 0.8246237888847535, 0.012067969072769765 with: {'C': 3, 'gamma': 'scale', 'kernel': 'poly'}
0.8600611691384386, 0.8838585601485555, 0.019320435290910504 with: {'C': 3, 'gamma': 'scale', 'kernel': 'rbf'}
0.8049364905328897, 0.8082496308124527, 0.008066089430052357 with: {'C': 3, 'gamma': 'scale', 'kernel': 'linear'}
0.8070825398657357, 0.8281133215673562, 0.012325743005626664 with: {'C': 3, 'gamma': 'auto', 'kernel': 'poly'}
0.8614929373368984, 0.885648149259824, 0.01984938016979657 with: {'C': 3, 'gamma': 'auto', 'kernel': 'rbf'}
0.8049364905328897, 0.8082496308124527, 0.008066089430052357 with: {'C': 3, 'gamma': 'auto', 'kernel': 'linear'}
0.8077987445579342, 0.8306188343678589, 0.01238102989712471 with: {'C': 4, 'gamma': 'scale', 'kernel': 'poly'}
0.8665031642526015, 0.8911958074701948, 0.01919770484314012 with: {'C': 4, 'gamma': 'scale', 'kernel': 'rbf'}
0.8042209270266284, 0.8088759489820829, 0.007702322075514746 with: {'C': 4, 'gamma': 'scale', 'kernel': 'linear'}
0.8113784856470527, 0.8360770071196167, 0.013880934002412498 with: {'C': 4, 'gamma': 'auto', 'kernel': 'poly'}
0.867577150697931, 0.8930749220604067, 0.021445271075986268 with: {'C': 4, 'gamma': 'auto', 'kernel': 'rbf'}
0.8042209270266284, 0.8088759489820829, 0.007702322075514746 with: {'C': 4, 'gamma': 'auto', 'kernel': 'linear'}
0.8113772032751777, 0.8396558251591808, 0.014442193893815264 with: {'C': 5, 'gamma': 'scale', 'kernel': 'poly'}
0.8693673418354588, 0.8960274219303406, 0.02111147735680577 with: {'C': 5, 'gamma': 'scale', 'kernel': 'rbf'}
0.8052942722860201, 0.8088759489820829, 0.009070098513506958 with: {'C': 5, 'gamma': 'scale', 'kernel': 'linear'}
0.8171029936971422, 0.845024672533647, 0.01799443680801489 with: {'C': 5, 'gamma': 'auto', 'kernel': 'poly'}
0.8729458005527022, 0.8976380801447135, 0.02269325086550227 with: {'C': 5, 'gamma': 'auto', 'kernel': 'rbf'}
0.8052942722860201, 0.8088759489820829, 0.009070098513506958 with: {'C': 5, 'gamma': 'auto', 'kernel': 'linear'}
In [ ]:
# evaluation results - accuracy
svc_tuning_accuracy = model_tuning_accuracy(X_train, y_train, X_test, y_test, params, model_type='svc')
svc_tuning_accuracy
Out[ ]:
Model CV Accuracy Accuracy_Train Accuracy_Test Diff Diff (%) Parameters
Parameter Set
param_1 SVC 0.771293 0.796349 0.795872 0.000478 0.059994 {'C': 1, 'gamma': 'scale', 'kernel': 'poly'}
param_2 SVC 0.832502 0.852899 0.743119 0.109780 12.871371 {'C': 1, 'gamma': 'scale', 'kernel': 'rbf'}
param_3 SVC 0.806012 0.807087 0.768349 0.038738 4.799731 {'C': 1, 'gamma': 'scale', 'kernel': 'linear'}
param_4 SVC 0.777734 0.800644 0.795872 0.004773 0.596105 {'C': 1, 'gamma': 'auto', 'kernel': 'poly'}
param_5 SVC 0.834291 0.854331 0.740826 0.113505 13.285841 {'C': 1, 'gamma': 'auto', 'kernel': 'rbf'}
param_6 SVC 0.806012 0.807087 0.768349 0.038738 4.799731 {'C': 1, 'gamma': 'auto', 'kernel': 'linear'}
param_7 SVC 0.794916 0.818898 0.807339 0.011558 1.411433 {'C': 2, 'gamma': 'scale', 'kernel': 'poly'}
param_8 SVC 0.850756 0.874374 0.738532 0.135842 15.535869 {'C': 2, 'gamma': 'scale', 'kernel': 'rbf'}
param_9 SVC 0.804579 0.807087 0.770642 0.036444 4.515552 {'C': 2, 'gamma': 'scale', 'kernel': 'linear'}
param_10 SVC 0.801000 0.824266 0.807339 0.016927 2.053564 {'C': 2, 'gamma': 'auto', 'kernel': 'poly'}
param_11 SVC 0.853261 0.876521 0.743119 0.133402 15.219468 {'C': 2, 'gamma': 'auto', 'kernel': 'rbf'}
param_12 SVC 0.804579 0.807087 0.770642 0.036444 4.515552 {'C': 2, 'gamma': 'auto', 'kernel': 'linear'}
param_13 SVC 0.804220 0.827845 0.800459 0.027387 3.308186 {'C': 3, 'gamma': 'scale', 'kernel': 'poly'}
param_14 SVC 0.860061 0.886543 0.750000 0.136543 15.401696 {'C': 3, 'gamma': 'scale', 'kernel': 'rbf'}
param_15 SVC 0.804936 0.807445 0.768349 0.039096 4.841930 {'C': 3, 'gamma': 'scale', 'kernel': 'linear'}
param_16 SVC 0.807083 0.832856 0.798165 0.034691 4.165303 {'C': 3, 'gamma': 'auto', 'kernel': 'poly'}
param_17 SVC 0.861493 0.886901 0.743119 0.143781 16.211653 {'C': 3, 'gamma': 'auto', 'kernel': 'rbf'}
param_18 SVC 0.804936 0.807445 0.768349 0.039096 4.841930 {'C': 3, 'gamma': 'auto', 'kernel': 'linear'}
param_19 SVC 0.807799 0.837867 0.800459 0.037408 4.464688 {'C': 4, 'gamma': 'scale', 'kernel': 'poly'}
param_20 SVC 0.866503 0.894417 0.759174 0.135242 15.120727 {'C': 4, 'gamma': 'scale', 'kernel': 'rbf'}
param_21 SVC 0.804221 0.807087 0.768349 0.038738 4.799731 {'C': 4, 'gamma': 'scale', 'kernel': 'linear'}
param_22 SVC 0.811378 0.842520 0.795872 0.046648 5.536740 {'C': 4, 'gamma': 'auto', 'kernel': 'poly'}
param_23 SVC 0.867577 0.894059 0.756881 0.137178 15.343284 {'C': 4, 'gamma': 'auto', 'kernel': 'rbf'}
param_24 SVC 0.804221 0.807087 0.768349 0.038738 4.799731 {'C': 4, 'gamma': 'auto', 'kernel': 'linear'}
param_25 SVC 0.811377 0.844667 0.798165 0.046502 5.505365 {'C': 5, 'gamma': 'scale', 'kernel': 'poly'}
param_26 SVC 0.869367 0.897280 0.759174 0.138106 15.391582 {'C': 5, 'gamma': 'scale', 'kernel': 'rbf'}
param_27 SVC 0.805294 0.807445 0.768349 0.039096 4.841930 {'C': 5, 'gamma': 'scale', 'kernel': 'linear'}
param_28 SVC 0.817103 0.849320 0.802752 0.046568 5.482937 {'C': 5, 'gamma': 'auto', 'kernel': 'poly'}
param_29 SVC 0.872946 0.899785 0.759174 0.140611 15.627167 {'C': 5, 'gamma': 'auto', 'kernel': 'rbf'}
param_30 SVC 0.805294 0.807445 0.768349 0.039096 4.841930 {'C': 5, 'gamma': 'auto', 'kernel': 'linear'}
In [ ]:
# filter parameter - accuracy
filter = (svc_tuning_accuracy['CV Accuracy'] > 0.8) & (svc_tuning_accuracy['Diff (%)'] < 15)
svc_tuning_accuracy[filter].sort_values(by=['CV Accuracy','Diff'], ascending=[False,True]).nlargest(3, columns=['CV Accuracy'])
Out[ ]:
Model CV Accuracy Accuracy_Train Accuracy_Test Diff Diff (%) Parameters
Parameter Set
param_5 SVC 0.834291 0.854331 0.740826 0.113505 13.285841 {'C': 1, 'gamma': 'auto', 'kernel': 'rbf'}
param_2 SVC 0.832502 0.852899 0.743119 0.109780 12.871371 {'C': 1, 'gamma': 'scale', 'kernel': 'rbf'}
param_28 SVC 0.817103 0.849320 0.802752 0.046568 5.482937 {'C': 5, 'gamma': 'auto', 'kernel': 'poly'}
In [ ]:
# get parameter
params_svc_accuracy = svc_tuning_precision.loc['param_5']
params_svc_accuracy['Parameters']
/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
  and should_run_async(code)
Out[ ]:
"{'C': 1, 'gamma': 'auto', 'kernel': 'rbf'}"

XGB Classifier¶

In [ ]:
# Define XGBClassifier model and parameters
model = XGBClassifier(max_depth=1)
objective = ['reg:squarederror','binary:logistic','reg:logistic']
learning_rate = [0.3, 0.5, 0.8, 1.0]
n_estimators = [50, 100, 150, 200]
subsample = [0.8, 0.9, 1.0]
colsample_bytree = [0.8, 0.9, 1.0]
gamma = [1,2,3]

# Define grid search
grid = dict(objective=objective, learning_rate=learning_rate, n_estimators=n_estimators, subsample=subsample, colsample_bytree=colsample_bytree, gamma=gamma)
grid_search = GridSearchCV(estimator=model, param_grid=grid, n_jobs=-1, cv=5, scoring='accuracy', error_score=0, return_train_score=True)
grid_result = grid_search.fit(X_train, y_train)

# Summarize results
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
means_test = grid_result.cv_results_['mean_test_score']
means_train = grid_result.cv_results_['mean_train_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']

for mean_test, mean_train, stdev, param in zip(means_test, means_train, stds, params):
    print(f"{mean_test}, {mean_train}, {stdev} with: {param}")
Best: 0.882618 using {'colsample_bytree': 1.0, 'gamma': 2, 'learning_rate': 0.8, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8342906239380359, 0.8498574475833724, 0.03424037737398742 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8324985092426953, 0.8499473332452887, 0.03417726205834697 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8339309186270928, 0.8477994821369256, 0.03606534289675377 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8239130295394361, 0.8422516238249032, 0.034306099403451785 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8246266694878848, 0.843594425968392, 0.03019681210137752 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8217612095331525, 0.8404625149575985, 0.03089184164594709 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8239130295394361, 0.8422516238249032, 0.034306099403451785 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8246266694878848, 0.843594425968392, 0.03019681210137752 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8217612095331525, 0.8404625149575985, 0.03089184164594709 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8496835747398389, 0.8723166568616858, 0.049585989510799205 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8464628977757259, 0.8696322531846178, 0.04754692889481234 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8410929655490795, 0.8651581403352904, 0.04760347967101518 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8418110937990907, 0.869363556686797, 0.04605576101869412 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8461038336507205, 0.8690058149539966, 0.042632958081117084 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8400196202896877, 0.8675742477178406, 0.04756464620092013 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8418110937990907, 0.869363556686797, 0.04605576101869412 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8461038336507205, 0.8690058149539966, 0.042632958081117084 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8400196202896877, 0.8675742477178406, 0.04756464620092013 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8511153429382986, 0.8776853441548307, 0.057969333512023935 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8521886881976904, 0.8725849931765337, 0.04714671048234928 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8410929655490795, 0.8651581403352904, 0.04760347967101518 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8532665217586448, 0.8860062511755972, 0.057703937939536994 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8518360359320599, 0.883858760250207, 0.058837625381218395 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8493277165445207, 0.8821589767601943, 0.05313790769173715 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8532665217586448, 0.8860062511755972, 0.057703937939536994 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8518360359320599, 0.883858760250207, 0.058837625381218395 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8493277165445207, 0.8821589767601943, 0.05313790769173715 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8504017029898499, 0.8777751497760862, 0.05696491425749697 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8486108706663845, 0.8766117187531265, 0.053907646498420926 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8410929655490795, 0.8651581403352904, 0.04760347967101518 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8611415674431429, 0.8943279586029703, 0.05970551101342759 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8607812209462622, 0.891911611098438, 0.0620925505234507 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8597059521290579, 0.8899433312122559, 0.06081593727192171 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8611415674431429, 0.8943279586029703, 0.05970551101342759 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8607812209462622, 0.891911611098438, 0.0620925505234507 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8597059521290579, 0.8899433312122559, 0.06081593727192171 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.3, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8389456338443585, 0.8678431443173131, 0.052189959383707074 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8439609902475619, 0.86793186938965, 0.04839248346855253 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8464616154038509, 0.8676634930544717, 0.047711474465645325 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 50, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8418104526131532, 0.8665897075714464, 0.043660429294280996 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8407377485396991, 0.8648896839594513, 0.046694997048139134 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8421688755522215, 0.8665006223161367, 0.047799188254988696 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 50, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8418104526131532, 0.8665897075714464, 0.043660429294280996 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8407377485396991, 0.8648896839594513, 0.046694997048139134 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8421688755522215, 0.8665006223161367, 0.047799188254988696 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 50, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8482556536570038, 0.8781323312242619, 0.056179137236159554 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.847898513089811, 0.8783115022431396, 0.05736824431323805 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8450317707632037, 0.8690051746287114, 0.0511035058479586 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 100, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8575611851680869, 0.8909272710536952, 0.06340822093832747 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 0.8}
0.854339225832099, 0.8881545425075938, 0.06071729193863114 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8557735587743089, 0.8871696021578963, 0.06110235706864902 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 100, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8575611851680869, 0.8909272710536952, 0.06340822093832747 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 0.8}
0.854339225832099, 0.8881545425075938, 0.06071729193863114 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8557735587743089, 0.8871696021578963, 0.06110235706864902 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 100, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8471823083976122, 0.8824274731563634, 0.061437225828470235 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8525509582523837, 0.8780433260096128, 0.055631975513188166 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8450317707632037, 0.8690051746287114, 0.0511035058479586 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 150, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8686581901885727, 0.9045277801122971, 0.07043720343021796 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8665076525541643, 0.900680745818876, 0.06587659809324685 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8640018979103751, 0.8962963185298131, 0.06762575743102071 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8686581901885727, 0.9045277801122971, 0.07043720343021796 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8665076525541643, 0.900680745818876, 0.06587659809324685 with: {'colsample_bytree': 0.8, 'gamma': 1, 'learning_rate': 0.5, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 0.9}
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0.8804720410871949, 0.9228716588026717, 0.08543154428575331 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.8}
0.8700918819448452, 0.9174136461322352, 0.08567124055807132 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8582825193477858, 0.8984440495771853, 0.0734723425698143 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8804720410871949, 0.9228716588026717, 0.08543154428575331 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 0.8}
0.8700918819448452, 0.9174136461322352, 0.08567124055807132 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8582825193477858, 0.8984440495771853, 0.0734723425698143 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 150, 'objective': 'reg:logistic', 'subsample': 1.0}
0.8300004488301564, 0.8514675455131206, 0.04784418221825642 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 0.8}
0.8325074858458205, 0.8506628967515496, 0.040312668941808114 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 0.9}
0.8196209308737441, 0.8419834876117067, 0.039672303396653605 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:squarederror', 'subsample': 1.0}
0.8786824911356044, 0.9305664477554598, 0.08891150538207061 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.8}
0.87152493251518, 0.9210823898540459, 0.08801286365255921 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 0.9}
0.8582825193477858, 0.8984440495771853, 0.0734723425698143 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'binary:logistic', 'subsample': 1.0}
0.8786824911356044, 0.9305664477554598, 0.08891150538207061 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 0.8}
0.87152493251518, 0.9210823898540459, 0.08801286365255921 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 0.9}
0.8582825193477858, 0.8984440495771853, 0.0734723425698143 with: {'colsample_bytree': 1.0, 'gamma': 3, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 1.0}
In [ ]:
xgb_tuning_accuracy = model_tuning_accuracy(X_train, y_train, X_test, y_test, params, model_type='xgbclassifier')
xgb_tuning_accuracy
Out[ ]:
Model CV Accuracy Accuracy_Train Accuracy_Test Diff Diff (%) Parameters
Parameter Set
param_1 XGBClassifier 0.833222 0.850036 0.798165 0.051871 6.102173 {'colsample_bytree': 0.8, 'gamma': 1, 'learnin...
param_2 XGBClassifier 0.832500 0.845025 0.791284 0.053741 6.359652 {'colsample_bytree': 0.8, 'gamma': 1, 'learnin...
param_3 XGBClassifier 0.831070 0.840730 0.788991 0.051739 6.154093 {'colsample_bytree': 0.8, 'gamma': 1, 'learnin...
param_4 XGBClassifier 0.821051 0.837151 0.782110 0.055041 6.574793 {'colsample_bytree': 0.8, 'gamma': 1, 'learnin...
param_5 XGBClassifier 0.818185 0.835719 0.782110 0.053609 6.414750 {'colsample_bytree': 0.8, 'gamma': 1, 'learnin...
... ... ... ... ... ... ... ...
param_1292 XGBClassifier 0.869739 0.915175 0.848624 0.066552 7.271997 {'colsample_bytree': 1.0, 'gamma': 3, 'learnin...
param_1293 XGBClassifier 0.858283 0.895132 0.832569 0.062564 6.989314 {'colsample_bytree': 1.0, 'gamma': 3, 'learnin...
param_1294 XGBClassifier 0.877971 0.922334 0.834862 0.087471 9.483682 {'colsample_bytree': 1.0, 'gamma': 3, 'learnin...
param_1295 XGBClassifier 0.869739 0.915175 0.848624 0.066552 7.271997 {'colsample_bytree': 1.0, 'gamma': 3, 'learnin...
param_1296 XGBClassifier 0.858283 0.895132 0.832569 0.062564 6.989314 {'colsample_bytree': 1.0, 'gamma': 3, 'learnin...

1296 rows × 7 columns

In [ ]:
# filter parameter xgb - accuracy
filter = (xgb_tuning_accuracy['CV Accuracy'] > 0.8) & (xgb_tuning_accuracy['Diff'] > 0)
xgb_tuning_accuracy[filter].sort_values(by=['CV Accuracy','Diff'], ascending=[False,True]).nlargest(3, columns=['CV Accuracy'])
Out[ ]:
Model CV Accuracy Accuracy_Train Accuracy_Test Diff Diff (%) Parameters
Parameter Set
param_1003 XGBClassifier 0.879048 0.924123 0.850917 0.073206 7.921638 {'colsample_bytree': 1.0, 'gamma': 1, 'learnin...
param_1006 XGBClassifier 0.879048 0.924123 0.850917 0.073206 7.921638 {'colsample_bytree': 1.0, 'gamma': 1, 'learnin...
param_247 XGBClassifier 0.878325 0.918754 0.837156 0.081599 8.881427 {'colsample_bytree': 0.8, 'gamma': 2, 'learnin...
In [ ]:
# get parameter
params_xgb_accuracy = xgb_tuning_accuracy.loc['param_1006']
params_xgb_accuracy['Parameters']
Out[ ]:
"{'colsample_bytree': 1.0, 'gamma': 1, 'learning_rate': 1.0, 'n_estimators': 200, 'objective': 'reg:logistic', 'subsample': 0.8}"

Adaboost Classifier¶

In [ ]:
# Define AdaBoostClassifier model and parameters
model = AdaBoostClassifier(random_state=42)
learning_rate = [0.01, 0.1, 0.2, 0.3, 0.5, 0.8, 1.0]
n_estimators = [50, 100, 150, 200]
algorithm = ['SAMME', 'SAMME.R']

# Define grid search
grid = dict(learning_rate=learning_rate, n_estimators=n_estimators, algorithm=algorithm)
grid_search = GridSearchCV(estimator=model, param_grid=grid, n_jobs=-1, cv=5, scoring='accuracy', error_score=0, return_train_score=True)
grid_result = grid_search.fit(X_train, y_train)

# Summarize results
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
means_test = grid_result.cv_results_['mean_test_score']
means_train = grid_result.cv_results_['mean_train_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']

for mean_test, mean_train, stdev, param in zip(means_test, means_train, stds, params):
    print(f"{mean_test}, {mean_train}, {stdev} with: {param}")
Best: 0.872244 using {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 150}
0.6929142542045768, 0.6924660927751296, 0.022290396790059352 with: {'algorithm': 'SAMME', 'learning_rate': 0.01, 'n_estimators': 50}
0.6893357954873334, 0.6955979237452625, 0.019427278743142755 with: {'algorithm': 'SAMME', 'learning_rate': 0.01, 'n_estimators': 100}
0.723326985592552, 0.7269175541174916, 0.022983702303155166 with: {'algorithm': 'SAMME', 'learning_rate': 0.01, 'n_estimators': 150}
0.7329986342739531, 0.7397999383686913, 0.022843292094760735 with: {'algorithm': 'SAMME', 'learning_rate': 0.01, 'n_estimators': 200}
0.776671732035573, 0.7899962781092794, 0.029176806883379917 with: {'algorithm': 'SAMME', 'learning_rate': 0.1, 'n_estimators': 50}
0.7967145632561987, 0.8095028274363376, 0.024384156915168185 with: {'algorithm': 'SAMME', 'learning_rate': 0.1, 'n_estimators': 100}
0.7995768172812434, 0.8185402984716236, 0.02507499853432255 with: {'algorithm': 'SAMME', 'learning_rate': 0.1, 'n_estimators': 150}
0.8078083623469972, 0.8220300712762082, 0.02554976194217574 with: {'algorithm': 'SAMME', 'learning_rate': 0.1, 'n_estimators': 200}
0.7949230897467956, 0.8079818547822294, 0.02532379002983386 with: {'algorithm': 'SAMME', 'learning_rate': 0.2, 'n_estimators': 50}
0.8053019665172704, 0.8220298311542263, 0.028239867597260212 with: {'algorithm': 'SAMME', 'learning_rate': 0.2, 'n_estimators': 100}
0.8174684696815231, 0.8334828092671076, 0.033223842246403554 with: {'algorithm': 'SAMME', 'learning_rate': 0.2, 'n_estimators': 150}
0.8221234795878456, 0.8403720289907273, 0.033551284150194746 with: {'algorithm': 'SAMME', 'learning_rate': 0.2, 'n_estimators': 200}
0.8031571995562994, 0.8162134364257042, 0.023864466986957194 with: {'algorithm': 'SAMME', 'learning_rate': 0.3, 'n_estimators': 50}
0.8253428741800833, 0.8354506889499864, 0.03173676303585906 with: {'algorithm': 'SAMME', 'learning_rate': 0.3, 'n_estimators': 100}
0.8339328421849052, 0.847709556454679, 0.036074851526161335 with: {'algorithm': 'SAMME', 'learning_rate': 0.3, 'n_estimators': 150}
0.8414481825584602, 0.8564784910734653, 0.04005016191334462 with: {'algorithm': 'SAMME', 'learning_rate': 0.3, 'n_estimators': 200}
0.8206897878315733, 0.8360776874652324, 0.0340868434394063 with: {'algorithm': 'SAMME', 'learning_rate': 0.5, 'n_estimators': 50}
0.8464628977757259, 0.8580890292268473, 0.038882707178386246 with: {'algorithm': 'SAMME', 'learning_rate': 0.5, 'n_estimators': 100}
0.85183347118831, 0.8716000528268362, 0.05144847569407908 with: {'algorithm': 'SAMME', 'learning_rate': 0.5, 'n_estimators': 150}
0.8518347535601849, 0.8768794547630195, 0.05330350611944286 with: {'algorithm': 'SAMME', 'learning_rate': 0.5, 'n_estimators': 200}
0.8418130173569033, 0.8589843640569409, 0.040426129931662405 with: {'algorithm': 'SAMME', 'learning_rate': 0.8, 'n_estimators': 50}
0.8511185488679862, 0.8800118860381074, 0.05738634406452961 with: {'algorithm': 'SAMME', 'learning_rate': 0.8, 'n_estimators': 100}
0.8564884810946326, 0.8835910242403141, 0.060681169161536874 with: {'algorithm': 'SAMME', 'learning_rate': 0.8, 'n_estimators': 150}
0.8643609620353807, 0.8864539185906442, 0.0613767439205307 with: {'algorithm': 'SAMME', 'learning_rate': 0.8, 'n_estimators': 200}
0.8493328460320209, 0.8724051418120405, 0.053225232874192376 with: {'algorithm': 'SAMME', 'learning_rate': 1.0, 'n_estimators': 50}
0.8568494687774507, 0.8840378512284239, 0.06780680730972728 with: {'algorithm': 'SAMME', 'learning_rate': 1.0, 'n_estimators': 100}
0.8657991420932156, 0.891732640181212, 0.07015451355231311 with: {'algorithm': 'SAMME', 'learning_rate': 1.0, 'n_estimators': 150}
0.8683010496213797, 0.8974595494511212, 0.07379998273016547 with: {'algorithm': 'SAMME', 'learning_rate': 1.0, 'n_estimators': 200}
0.6904097819326627, 0.6954188327670456, 0.01824768136217668 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.01, 'n_estimators': 50}
0.7304935208161014, 0.7372049401095756, 0.020262502511329127 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.01, 'n_estimators': 100}
0.7469546873897961, 0.7546536040308476, 0.025795815496947108 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.01, 'n_estimators': 150}
0.7623553324228494, 0.7715641145702017, 0.027664189232246186 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.01, 'n_estimators': 200}
0.7985002660921641, 0.8095031475989802, 0.024724269128119168 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.1, 'n_estimators': 50}
0.8103102698751611, 0.8264144985652712, 0.027465015150397558 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.1, 'n_estimators': 100}
0.8228364783503567, 0.8413573695437282, 0.03334279687343186 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.1, 'n_estimators': 150}
0.8314264463551787, 0.8537050421614178, 0.040555611605451555 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.1, 'n_estimators': 200}
0.8088785016767013, 0.8275781697102127, 0.02345346571473472 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.2, 'n_estimators': 50}
0.8339328421849052, 0.8556738823322247, 0.04124050430340257 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.2, 'n_estimators': 100}
0.8428837978725451, 0.8690060550759785, 0.05033212262601407 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.2, 'n_estimators': 150}
0.8486147177820097, 0.8779539606119909, 0.062058201680318704 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.2, 'n_estimators': 200}
0.8249850924269528, 0.8443095492510195, 0.03361266003644814 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.3, 'n_estimators': 50}
0.8457479754554024, 0.8730328206729018, 0.052751905746885104 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.3, 'n_estimators': 100}
0.8529113047492641, 0.884754375222613, 0.0616095333330887 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.3, 'n_estimators': 150}
0.8600682221837511, 0.891554269568941, 0.06547038997225749 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.3, 'n_estimators': 200}
0.8378703650271543, 0.8690956605955826, 0.04667312317080158 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.5, 'n_estimators': 50}
0.857206609344644, 0.8888699459325335, 0.06825361527914772 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.5, 'n_estimators': 100}
0.8672283455479255, 0.898533134832495, 0.07061499684419591 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.5, 'n_estimators': 150}
0.8683004084354422, 0.9042599640617434, 0.07919705569257635 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.5, 'n_estimators': 200}
0.8457531049429023, 0.8822487823814498, 0.06578808134188835 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.8, 'n_estimators': 50}
0.8700893172010952, 0.903812976992312, 0.07208159711744441 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.8, 'n_estimators': 100}
0.8704503048839133, 0.913924313551284, 0.08920646019723158 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.8, 'n_estimators': 150}
0.8708080866370439, 0.9208139734985373, 0.09482543335119589 with: {'algorithm': 'SAMME.R', 'learning_rate': 0.8, 'n_estimators': 200}
0.8611422086290803, 0.8884226787207903, 0.06767385021534016 with: {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 50}
0.8700931643167202, 0.9085555862378089, 0.07929995167700161 with: {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 100}
0.8722437019511288, 0.917771587966687, 0.09380053624971159 with: {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 150}
0.8718891261276858, 0.92546697722443, 0.10553848227519459 with: {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 200}
In [ ]:
adb_tuning_accuracy = model_tuning_accuracy(X_train, y_train, X_test, y_test, params, model_type='adaboost')
adb_tuning_accuracy
Out[ ]:
Model CV Accuracy Accuracy_Train Accuracy_Test Diff Diff (%) Parameters
Parameter Set
param_1 AdaBoostClassifier 0.692914 0.692198 0.527523 0.164675 23.790120 {'algorithm': 'SAMME', 'learning_rate': 0.01, ...
param_2 AdaBoostClassifier 0.689336 0.694345 0.525229 0.169116 24.356143 {'algorithm': 'SAMME', 'learning_rate': 0.01, ...
param_3 AdaBoostClassifier 0.723327 0.734431 0.651376 0.083055 11.308725 {'algorithm': 'SAMME', 'learning_rate': 0.01, ...
param_4 AdaBoostClassifier 0.732999 0.734789 0.649083 0.085706 11.664067 {'algorithm': 'SAMME', 'learning_rate': 0.01, ...
param_5 AdaBoostClassifier 0.776672 0.780601 0.715596 0.065005 8.327549 {'algorithm': 'SAMME', 'learning_rate': 0.1, '...
param_6 AdaBoostClassifier 0.796715 0.802792 0.745413 0.057379 7.147415 {'algorithm': 'SAMME', 'learning_rate': 0.1, '...
param_7 AdaBoostClassifier 0.799577 0.818898 0.740826 0.078072 9.533786 {'algorithm': 'SAMME', 'learning_rate': 0.1, '...
param_8 AdaBoostClassifier 0.807808 0.823193 0.745413 0.077780 9.448544 {'algorithm': 'SAMME', 'learning_rate': 0.1, '...
param_9 AdaBoostClassifier 0.794923 0.810308 0.743119 0.067189 8.291730 {'algorithm': 'SAMME', 'learning_rate': 0.2, '...
param_10 AdaBoostClassifier 0.805302 0.816034 0.766055 0.049979 6.124658 {'algorithm': 'SAMME', 'learning_rate': 0.2, '...
param_11 AdaBoostClassifier 0.817468 0.834646 0.772936 0.061710 7.393543 {'algorithm': 'SAMME', 'learning_rate': 0.2, '...
param_12 AdaBoostClassifier 0.822123 0.841804 0.784404 0.057400 6.818714 {'algorithm': 'SAMME', 'learning_rate': 0.2, '...
param_13 AdaBoostClassifier 0.803157 0.816750 0.747706 0.069044 8.453473 {'algorithm': 'SAMME', 'learning_rate': 0.3, '...
param_14 AdaBoostClassifier 0.825343 0.837151 0.779817 0.057335 6.848767 {'algorithm': 'SAMME', 'learning_rate': 0.3, '...
param_15 AdaBoostClassifier 0.833933 0.846457 0.779817 0.066640 7.872840 {'algorithm': 'SAMME', 'learning_rate': 0.3, '...
param_16 AdaBoostClassifier 0.841448 0.855404 0.791284 0.064120 7.495873 {'algorithm': 'SAMME', 'learning_rate': 0.3, '...
param_17 AdaBoostClassifier 0.820690 0.832856 0.779817 0.053040 6.368400 {'algorithm': 'SAMME', 'learning_rate': 0.5, '...
param_18 AdaBoostClassifier 0.846463 0.857194 0.793578 0.063616 7.421425 {'algorithm': 'SAMME', 'learning_rate': 0.5, '...
param_19 AdaBoostClassifier 0.851833 0.871868 0.800459 0.071410 8.190408 {'algorithm': 'SAMME', 'learning_rate': 0.5, '...
param_20 AdaBoostClassifier 0.851835 0.876163 0.814220 0.061943 7.069804 {'algorithm': 'SAMME', 'learning_rate': 0.5, '...
param_21 AdaBoostClassifier 0.841813 0.855404 0.788991 0.066414 7.764001 {'algorithm': 'SAMME', 'learning_rate': 0.8, '...
param_22 AdaBoostClassifier 0.851119 0.880816 0.805046 0.075770 8.602269 {'algorithm': 'SAMME', 'learning_rate': 0.8, '...
param_23 AdaBoostClassifier 0.856488 0.887258 0.823394 0.063864 7.197893 {'algorithm': 'SAMME', 'learning_rate': 0.8, '...
param_24 AdaBoostClassifier 0.864361 0.891195 0.834862 0.056333 6.321064 {'algorithm': 'SAMME', 'learning_rate': 0.8, '...
param_25 AdaBoostClassifier 0.849333 0.871868 0.798165 0.073703 8.453473 {'algorithm': 'SAMME', 'learning_rate': 1.0, '...
param_26 AdaBoostClassifier 0.856849 0.881532 0.809633 0.071899 8.156123 {'algorithm': 'SAMME', 'learning_rate': 1.0, '...
param_27 AdaBoostClassifier 0.865799 0.887258 0.821101 0.066157 7.456395 {'algorithm': 'SAMME', 'learning_rate': 1.0, '...
param_28 AdaBoostClassifier 0.868301 0.892985 0.830275 0.062710 7.022485 {'algorithm': 'SAMME', 'learning_rate': 1.0, '...
param_29 AdaBoostClassifier 0.690410 0.694703 0.525229 0.169474 24.395115 {'algorithm': 'SAMME.R', 'learning_rate': 0.01...
param_30 AdaBoostClassifier 0.730494 0.735863 0.646789 0.089074 12.104648 {'algorithm': 'SAMME.R', 'learning_rate': 0.01...
param_31 AdaBoostClassifier 0.746955 0.748389 0.649083 0.099307 13.269407 {'algorithm': 'SAMME.R', 'learning_rate': 0.01...
param_32 AdaBoostClassifier 0.762355 0.767359 0.678899 0.088460 11.527797 {'algorithm': 'SAMME.R', 'learning_rate': 0.01...
param_33 AdaBoostClassifier 0.798500 0.809592 0.736239 0.073353 9.060546 {'algorithm': 'SAMME.R', 'learning_rate': 0.1,...
param_34 AdaBoostClassifier 0.810310 0.818540 0.756881 0.061659 7.532804 {'algorithm': 'SAMME.R', 'learning_rate': 0.1,...
param_35 AdaBoostClassifier 0.822836 0.836435 0.777523 0.058912 7.043257 {'algorithm': 'SAMME.R', 'learning_rate': 0.1,...
param_36 AdaBoostClassifier 0.831426 0.850394 0.791284 0.059109 6.950815 {'algorithm': 'SAMME.R', 'learning_rate': 0.1,...
param_37 AdaBoostClassifier 0.808879 0.823550 0.779817 0.043734 5.310415 {'algorithm': 'SAMME.R', 'learning_rate': 0.2,...
param_38 AdaBoostClassifier 0.833933 0.851110 0.788991 0.062119 7.298555 {'algorithm': 'SAMME.R', 'learning_rate': 0.2,...
param_39 AdaBoostClassifier 0.842884 0.866500 0.800459 0.066041 7.621576 {'algorithm': 'SAMME.R', 'learning_rate': 0.2,...
param_40 AdaBoostClassifier 0.848615 0.876163 0.805046 0.071117 8.116905 {'algorithm': 'SAMME.R', 'learning_rate': 0.2,...
param_41 AdaBoostClassifier 0.824985 0.843593 0.791284 0.052309 6.200737 {'algorithm': 'SAMME.R', 'learning_rate': 0.3,...
param_42 AdaBoostClassifier 0.845748 0.862205 0.800459 0.061746 7.161409 {'algorithm': 'SAMME.R', 'learning_rate': 0.3,...
param_43 AdaBoostClassifier 0.852911 0.878311 0.807339 0.070971 8.080423 {'algorithm': 'SAMME.R', 'learning_rate': 0.3,...
param_44 AdaBoostClassifier 0.860068 0.886901 0.823394 0.063506 7.160443 {'algorithm': 'SAMME.R', 'learning_rate': 0.3,...
param_45 AdaBoostClassifier 0.837870 0.862921 0.798165 0.064755 7.504214 {'algorithm': 'SAMME.R', 'learning_rate': 0.5,...
param_46 AdaBoostClassifier 0.857207 0.885111 0.805046 0.080065 9.045768 {'algorithm': 'SAMME.R', 'learning_rate': 0.5,...
param_47 AdaBoostClassifier 0.867228 0.892985 0.821101 0.071884 8.049861 {'algorithm': 'SAMME.R', 'learning_rate': 0.5,...
param_48 AdaBoostClassifier 0.868300 0.897996 0.825688 0.072308 8.052113 {'algorithm': 'SAMME.R', 'learning_rate': 0.5,...
param_49 AdaBoostClassifier 0.845753 0.881174 0.811927 0.069247 7.858532 {'algorithm': 'SAMME.R', 'learning_rate': 0.8,...
param_50 AdaBoostClassifier 0.870089 0.896922 0.807339 0.089583 9.987772 {'algorithm': 'SAMME.R', 'learning_rate': 0.8,...
param_51 AdaBoostClassifier 0.870450 0.906228 0.823394 0.082833 9.140434 {'algorithm': 'SAMME.R', 'learning_rate': 0.8,...
param_52 AdaBoostClassifier 0.870808 0.912312 0.830275 0.082037 8.992193 {'algorithm': 'SAMME.R', 'learning_rate': 0.8,...
param_53 AdaBoostClassifier 0.861142 0.879742 0.816514 0.063229 7.187166 {'algorithm': 'SAMME.R', 'learning_rate': 1.0,...
param_54 AdaBoostClassifier 0.870093 0.900859 0.811927 0.088932 9.871953 {'algorithm': 'SAMME.R', 'learning_rate': 1.0,...
param_55 AdaBoostClassifier 0.872244 0.910523 0.834862 0.075660 8.309532 {'algorithm': 'SAMME.R', 'learning_rate': 1.0,...
param_56 AdaBoostClassifier 0.871889 0.918397 0.850917 0.067479 7.347494 {'algorithm': 'SAMME.R', 'learning_rate': 1.0,...
In [ ]:
# filter parameter - accuracy
filter = (adb_tuning_accuracy['CV Accuracy'] > 0.8) & (adb_tuning_accuracy['Diff'] > 0)
adb_tuning_accuracy[filter].sort_values(by=['CV Accuracy','Diff'], ascending=[False,True]).nlargest(3, columns=['CV Accuracy'])
Out[ ]:
Model CV Accuracy Accuracy_Train Accuracy_Test Diff Diff (%) Parameters
Parameter Set
param_55 AdaBoostClassifier 0.872244 0.910523 0.834862 0.075660 8.309532 {'algorithm': 'SAMME.R', 'learning_rate': 1.0,...
param_56 AdaBoostClassifier 0.871889 0.918397 0.850917 0.067479 7.347494 {'algorithm': 'SAMME.R', 'learning_rate': 1.0,...
param_52 AdaBoostClassifier 0.870808 0.912312 0.830275 0.082037 8.992193 {'algorithm': 'SAMME.R', 'learning_rate': 0.8,...
In [ ]:
# get parameter
params_adb_accuracy = adb_tuning_accuracy.loc['param_56']
params_adb_accuracy['Parameters']
Out[ ]:
"{'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 200}"

5.2 Business Recommendation

5.2.1 Business Simulation¶

In [ ]:
from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay
params = {'algorithm': 'SAMME.R', 'learning_rate': 1.0, 'n_estimators': 200}
adaboost_model = AdaBoostClassifier(**params, random_state=42)
adaboost_model.fit(X_train, y_train)
y_pred = adaboost_model.predict(X_test)
y_pred_train = adaboost_model.predict(X_train)
print('Classification Report on Test Data')
print(classification_report(y_test, y_pred))
print('\n')
print('Classification Report on Train Data')
print(classification_report(y_train, y_pred_train))
Classification Report on Test Data
              precision    recall  f1-score   support

           0       0.90      0.92      0.91       367
           1       0.53      0.46      0.50        69

    accuracy                           0.85       436
   macro avg       0.72      0.69      0.70       436
weighted avg       0.84      0.85      0.85       436



Classification Report on Train Data
              precision    recall  f1-score   support

           0       0.91      0.93      0.92      1397
           1       0.93      0.91      0.92      1397

    accuracy                           0.92      2794
   macro avg       0.92      0.92      0.92      2794
weighted avg       0.92      0.92      0.92      2794

In [ ]:
from sklearn.utils.class_weight import compute_sample_weight
from sklearn.metrics import confusion_matrix

# Compute confusion matrix
cm = confusion_matrix(y_test, y_pred)
tn, fp, fn, tp = cm.ravel()

# Define names for confusion matrix elements
names = ['True Negative', 'False Positive', 'False Negative', 'True Positive']

# Convert counts to string format
counts = ['{0:0.0f}'.format(value) for value in cm.flatten()]

# Create labels with counts and percentages
labels = [f'{v1}\n\n{v2}' for v1, v2 in zip(names, counts)]
labels = np.asarray(labels).reshape(2, 2)

# Create heatmap with annotations
heatmap = sns.heatmap(cm, annot=labels, annot_kws={'size': 13}, fmt='', cmap='coolwarm', center=100)

# Customize tick labels and rotations
heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=13)
heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=0, ha='right', fontsize=13)

# Set title, ylabel, and xlabel
plt.title(f'Confusion Matrix\nPrecision', fontsize=13, color='black')
plt.ylabel('Actual Label', fontsize=13)
plt.xlabel('\nPredicted Label', fontsize=13)

# Show the plot
plt.show()
In [ ]:
# menggunakan data awal
cost = 3  # asumsi cost per customer ($3)
revenue = 11  # asumsi revenue per customer ($11)
total_customer = 2240
total_response = 334
rate_accept = total_response/total_customer * 100
total_cost = total_customer * cost
total_revenue = total_response * revenue
total_profit = total_revenue - total_cost

profit_margin = (total_profit/total_revenue) * 100

print(f'total customer: {total_customer}')
print(f'total response: {total_response}')
print(f'rate accept: {rate_accept:.2f}%')
print(f'total cost: {total_cost}')
print(f'total revenue: {total_revenue}')
print(f'total profit: {total_profit}')
print(f'profit margin before pemodelan: {profit_margin:.2f}%')
total customer: 2240
total response: 334
rate accept: 14.91%
total cost: 6720
total revenue: 3674
total profit: -3046
profit margin before pemodelan: -82.91%
In [ ]:
# menggunakan data test - tanpa sample weight
cost = 3  # asumsi cost per customer ($3)
revenue = 11  # asumsi revenue per customer ($11)
total_customer = tp + fp
total_response = tp
rate_accept = total_response/total_customer * 100
total_cost = total_customer * cost
total_revenue = total_response * revenue
total_profit = total_revenue - total_cost

profit_margin = ((total_profit)/total_revenue) * 100

print(f'total customer: {total_customer}')
print(f'total response: {total_response}')
print(f'rate accept: {rate_accept:.2f}%')
print(f'total cost: {total_cost}')
print(f'total revenue: {total_revenue}')
print(f'total profit: {total_profit}')
print(f'profit margin after pemodelan: {profit_margin:.2f}%')
total customer: 60
total response: 32
rate accept: 53.33%
total cost: 180
total revenue: 352
total profit: 172
profit margin after pemodelan: 48.86%
  • Pada table confusion matrix terdapat nilai sample response dari customer true positive sebanyak 32 dan false positive 28 customer .

  • Sebelum dilakukan modeling presentase response rate berada di angka 14.91% setelah dilakukan modeling response rate meningkat menjadi 53.33% ada peningkatan sebesar 38%.

  • Presentase Profit margin sebelum dilakukan pemodelan sebesar -82.91% setelah dilakukan pemodelan menggunakan data test presentase profit margin naik menjadi 48.86%.

  • Setelah dilakukan modeling cost campaign menjadi lebih efisien dikarenakan lebih targeted pada segment customer tertentu menjadi 180 dollar dari total customer yang benar benar meresponse campaign (true positive)dengan asumsi cost yang dikeluarkan 3 dollar untuk setiap customer dan revenue yang didapatkan $11/ customer.

5.2.2 Business Insights & Recommendation¶

In [ ]:
# get clean data from data preprocessing based on selected features in modelling
data_train = pd.merge(data_before_transform_train[selected_features], y_train, left_index=True, right_index=True)
data_test = pd.merge(data_before_transform_test[selected_features], y_test, left_index=True, right_index=True)
data_clean = pd.concat([data_train,data_test])
data_clean['RFM_Cat'] = oe_rfm.inverse_transform(data_clean[['RFM_Cat']])
data_clean['Education'] = oe_edu.inverse_transform(data_clean[['Education']])
data_clean.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 2084 entries, 0 to 435
Data columns (total 16 columns):
 #   Column               Non-Null Count  Dtype 
---  ------               --------------  ----- 
 0   AcceptedCmp1         2084 non-null   int64 
 1   AcceptedCmp2         2084 non-null   int64 
 2   AcceptedCmp3         2084 non-null   int64 
 3   AcceptedCmp4         2084 non-null   int64 
 4   AcceptedCmp5         2084 non-null   int64 
 5   Customer_Lifespan    2084 non-null   int64 
 6   Education            2084 non-null   object
 7   MntFishProducts      2084 non-null   int64 
 8   MntFruits            2084 non-null   int64 
 9   MntGoldProds         2084 non-null   int64 
 10  MntSweetProducts     2084 non-null   int64 
 11  NumCatalogPurchases  2084 non-null   int64 
 12  NumWebPurchases      2084 non-null   int64 
 13  RFM_Cat              2084 non-null   object
 14  Recency              2084 non-null   int64 
 15  Response             2084 non-null   int64 
dtypes: int64(14), object(2)
memory usage: 276.8+ KB
In [ ]:
data = data_clean.copy()
data.head()
Out[ ]:
AcceptedCmp1 AcceptedCmp2 AcceptedCmp3 AcceptedCmp4 AcceptedCmp5 Customer_Lifespan Education MntFishProducts MntFruits MntGoldProds MntSweetProducts NumCatalogPurchases NumWebPurchases RFM_Cat Recency Response
0 0 0 1 0 0 347 Graduation 12 8 13 2 0 2 at risk cust 49 0
1 0 0 0 0 0 525 Master 151 15 15 38 3 5 loyal cust 30 0
2 0 0 0 1 0 623 PhD 0 0 10 0 4 3 loyal cust 59 0
3 0 0 0 0 0 418 PhD 0 0 2 0 0 1 at risk cust 43 0
4 0 0 0 0 0 269 Graduation 2 4 321 4 0 25 loyal cust 0 0
In [ ]:
# 10 feature importance based on adaboost model (hyper parameter tuning)
importance_adb.nlargest(10)
Out[ ]:
NumWebPurchases        0.235
Education              0.175
RFM_Cat                0.125
NumCatalogPurchases    0.100
MntFishProducts        0.070
MntGoldProds           0.070
MntSweetProducts       0.055
Customer_Lifespan      0.050
Recency                0.050
MntFruits              0.030
dtype: float64
In [ ]:
# Menghitung frekuensi kategori dan mengurutkannya dari besar ke kecil
rfm_counts = data['RFM_Cat'].value_counts().sort_values(ascending=False)

# Membuat subplot
fig, axes = plt.subplots(1, 2, figsize=(12, 6))

# Plot countplot tanpa bingkai
sns.countplot(x=data['RFM_Cat'], order=rfm_counts.index,
              ax=axes[0], palette=sns.color_palette())
axes[0].set_title('Proportions of Customer Segmentation')

# Menambahkan nilai label pada countplot
for p in axes[0].patches:
    axes[0].annotate(f'{p.get_height():0.0f}', (p.get_x() + p.get_width() / 2., p.get_height()),
                     ha='center', va='center', xytext=(0, 5), textcoords='offset points')

# Plot pie plot
axes[1].pie(rfm_counts, labels=rfm_counts.index,
            autopct='%1.0f%%', startangle=140, textprops={'fontsize': 12, 'color': 'black'})
axes[1].set_title('Percentages of Customer Segmentation')

plt.tight_layout()
plt.show()
/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
  and should_run_async(code)
In [ ]:
# Menghitung frekuensi kategori dan mengurutkannya dari besar ke kecil
education_counts = data['Education'].value_counts().sort_values(ascending=False)

fig, axes = plt.subplots(1, 2, figsize=(12, 6))

sns.countplot(x=data['Education'], order=education_counts.index, ax=axes[0], palette=sns.color_palette())
axes[0].set_title('Proportions of Customer Education Level')

for p in axes[0].patches:
    axes[0].annotate(f'{p.get_height():0.0f}', (p.get_x() + p.get_width() / 2., p.get_height()),
                     ha='center', va='center', xytext=(0, 5), textcoords='offset points')

# Plot pie plot
axes[1].pie(education_counts, labels=education_counts.index, autopct='%1.0f%%', startangle=140, textprops={'fontsize': 12, 'color': 'black'})
axes[1].set_title('Percentages of Customer Education Level')

plt.tight_layout()
plt.show()
/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
  and should_run_async(code)
In [ ]:
# 'Education'
education_counts = data.groupby('Education')['Response'].value_counts(normalize=True).unstack()
colors = ['#e41a1c', '#377eb8']

fig, axes = plt.subplots(1, 2, figsize=(12, 6))

education_counts.plot(kind='bar', stacked=True, color=colors, ax=axes[0])
axes[0].set_title('Percentage of Response by Education')
axes[0].set_ylabel('Percentage')
axes[0].set_xticklabels(education_counts.index, rotation=0, ha='center')

handles, labels = axes[0].get_legend_handles_labels()
axes[0].legend(reversed(handles), reversed(labels), bbox_to_anchor=(
    1.2, 1), loc='upper right', title='Response')

for p in axes[0].patches:
    if p.get_height() >= 0.5:
        vertical_position = 'bottom' if p.get_height() < 0.5 else 'top'
        axes[0].annotate(f'{p.get_height():.0%}', (p.get_x() + p.get_width() / 2., p.get_height()),
                         ha='center', va=vertical_position, xytext=(0, 10 if p.get_height() < 0.5 else -10),
                         fontsize=12, color='white',
                         textcoords='offset points')
for p in axes[0].patches:
    if p.get_height() < 0.5:
        axes[0].annotate(f'{p.get_height():.0%}', (p.get_x() + p.get_width() / 2., 1),
                         ha='center', va='top', xytext=(0, -1),
                         fontsize=12, color='white',
                         textcoords='offset points')

# 'RFM_Cat'
rfm_counts = data.groupby('RFM_Cat')['Response'].value_counts(normalize=True).unstack()
rfm_counts.plot(kind='bar', stacked=True, color=colors, ax=axes[1])
axes[1].set_title('Percentage of Response by RFM_Cat')
axes[1].set_ylabel('Percentage')
axes[1].set_xticklabels(rfm_counts.index, rotation=0, ha='center')

handles, labels = axes[1].get_legend_handles_labels()
axes[1].legend(reversed(handles), reversed(labels), bbox_to_anchor=(
    1.2, 1), loc='upper right', title='Response')

for p in axes[1].patches:
    if p.get_height() >= 0.5:
        vertical_position = 'bottom' if p.get_height() < 0.5 else 'top'
        axes[1].annotate(f'{p.get_height():.0%}', (p.get_x() + p.get_width() / 2., p.get_height()),
                         ha='center', va=vertical_position, xytext=(0, 10 if p.get_height() < 0.5 else -10),
                         fontsize=12, color='white',
                         textcoords='offset points')
for p in axes[1].patches:
    if p.get_height() <= 0.5:
        axes[1].annotate(f'{p.get_height():.0%}', (p.get_x() + p.get_width() / 2., 1),
                         ha='center', va='top', xytext=(0, -1),
                         fontsize=12, color='white',
                         textcoords='offset points')

plt.tight_layout()
plt.show()
In [ ]:
# graduation education level
filter_graduation = (data['Education'] == 'Graduation') & (data['RFM_Cat'] == 'loyal cust') & (data['Response'] == 1)
filtered_data_graduation = data[filter_graduation][['Customer_Lifespan', 'Recency', 'NumCatalogPurchases', 'NumWebPurchases',
                                                    'MntFishProducts', 'MntFruits', 'MntGoldProds', 'MntSweetProducts']]
In [ ]:
from scipy.stats import t

confidence_level = 0.95

# Calculate confidence interval for each variable
print("Confidence Interval Mean of Customer in Graduation Education Level")
for column, values in filtered_data_graduation.items():
    sample_mean = np.mean(values)
    sample_std = np.std(values)
    degrees_of_freedom = len(values) - 1
    alpha = 1 - confidence_level
    t_value = t.ppf(1 - alpha / 2, df=degrees_of_freedom)
    margin_of_error = t_value * (sample_std / np.sqrt(len(values)))
    lower_bound = sample_mean - margin_of_error
    upper_bound = sample_mean + margin_of_error
    print(
        f"Confidence Interval for {column}: ({lower_bound:.2f}, {upper_bound:.2f})")
Confidence Interval Mean of Customer in Graduation Education Level
Confidence Interval for Customer_Lifespan: (393.45, 494.46)
Confidence Interval for Recency: (32.72, 46.01)
Confidence Interval for NumCatalogPurchases: (4.07, 5.64)
Confidence Interval for NumWebPurchases: (4.77, 5.67)
Confidence Interval for MntFishProducts: (53.12, 86.67)
Confidence Interval for MntFruits: (39.56, 63.70)
Confidence Interval for MntGoldProds: (60.24, 92.58)
Confidence Interval for MntSweetProducts: (34.38, 58.68)
In [ ]:
# filter apriori
filter_apriori_graduation = (data['Education'] == 'Graduation') & (data['RFM_Cat'] == 'loyal cust') & (data['Response'] == 1)

# web - apriori
web_selling_graduation = data[filter_apriori_graduation][['NumWebPurchases', 'MntFishProducts', 'MntFruits', 'MntGoldProds', 'MntSweetProducts']]

# catalog - aprioroi
catalog_selling_graduation = data[filter_apriori_graduation][['NumCatalogPurchases', 'MntFishProducts', 'MntFruits', 'MntGoldProds', 'MntSweetProducts']]
In [ ]:
from mlxtend.frequent_patterns import apriori
from mlxtend.frequent_patterns import association_rules

# Encode data dalam format one-hot encoding
web_encoded_graduation = web_selling_graduation.astype(bool).astype(int)
catalog_encoded_graduation = catalog_selling_graduation.astype(bool).astype(int)

# Terapkan algoritma Apriori untuk menemukan item-item yang sering muncul bersama-sama
frequent_itemsets_web_graduation = apriori(web_encoded_graduation, min_support=0.1, use_colnames=True)
frequent_itemsets_catalog_graduation = apriori(catalog_encoded_graduation, min_support=0.1, use_colnames=True)

# Temukan association rules
rules_web_graduation = association_rules(frequent_itemsets_web_graduation, metric="lift", min_threshold=1)
rules_catalog_graduation = association_rules(frequent_itemsets_catalog_graduation, metric="lift", min_threshold=1)
/usr/local/lib/python3.10/dist-packages/mlxtend/frequent_patterns/fpcommon.py:110: DeprecationWarning: DataFrames with non-bool types result in worse computationalperformance and their support might be discontinued in the future.Please use a DataFrame with bool type
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/mlxtend/frequent_patterns/fpcommon.py:110: DeprecationWarning: DataFrames with non-bool types result in worse computationalperformance and their support might be discontinued in the future.Please use a DataFrame with bool type
  warnings.warn(
In [ ]:
pd.set_option('display.max_colwidth', 100)
frequent_itemsets_web_graduation[frequent_itemsets_web_graduation['itemsets'].apply(lambda x: 'NumWebPurchases' in x)]
/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
  and should_run_async(code)
Out[ ]:
support itemsets
0 1.000000 (NumWebPurchases)
5 0.897059 (NumWebPurchases, MntFishProducts)
6 0.926471 (MntFruits, NumWebPurchases)
7 0.955882 (NumWebPurchases, MntGoldProds)
8 0.823529 (NumWebPurchases, MntSweetProducts)
15 0.852941 (MntFruits, NumWebPurchases, MntFishProducts)
16 0.882353 (MntGoldProds, NumWebPurchases, MntFishProducts)
17 0.794118 (MntSweetProducts, NumWebPurchases, MntFishProducts)
18 0.882353 (MntFruits, NumWebPurchases, MntGoldProds)
19 0.794118 (MntFruits, NumWebPurchases, MntSweetProducts)
20 0.808824 (MntSweetProducts, NumWebPurchases, MntGoldProds)
25 0.838235 (MntGoldProds, MntFruits, NumWebPurchases, MntFishProducts)
26 0.764706 (MntFruits, MntSweetProducts, NumWebPurchases, MntFishProducts)
27 0.779412 (MntGoldProds, MntSweetProducts, NumWebPurchases, MntFishProducts)
28 0.779412 (MntFruits, MntSweetProducts, NumWebPurchases, MntGoldProds)
30 0.750000 (MntSweetProducts, NumWebPurchases, MntFishProducts, MntFruits, MntGoldProds)
In [ ]:
pd.set_option('display.max_colwidth', 100)
frequent_itemsets_catalog_graduation[frequent_itemsets_catalog_graduation['itemsets'].apply(lambda x: 'NumCatalogPurchases' in x)]
/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
  and should_run_async(code)
Out[ ]:
support itemsets
0 1.000000 (NumCatalogPurchases)
5 0.897059 (NumCatalogPurchases, MntFishProducts)
6 0.926471 (MntFruits, NumCatalogPurchases)
7 0.955882 (NumCatalogPurchases, MntGoldProds)
8 0.823529 (NumCatalogPurchases, MntSweetProducts)
15 0.852941 (MntFruits, NumCatalogPurchases, MntFishProducts)
16 0.882353 (MntGoldProds, NumCatalogPurchases, MntFishProducts)
17 0.794118 (NumCatalogPurchases, MntSweetProducts, MntFishProducts)
18 0.882353 (MntFruits, NumCatalogPurchases, MntGoldProds)
19 0.794118 (MntFruits, NumCatalogPurchases, MntSweetProducts)
20 0.808824 (NumCatalogPurchases, MntSweetProducts, MntGoldProds)
25 0.838235 (MntGoldProds, MntFruits, NumCatalogPurchases, MntFishProducts)
26 0.764706 (MntFruits, NumCatalogPurchases, MntSweetProducts, MntFishProducts)
27 0.779412 (MntGoldProds, NumCatalogPurchases, MntSweetProducts, MntFishProducts)
28 0.779412 (MntFruits, NumCatalogPurchases, MntSweetProducts, MntGoldProds)
30 0.750000 (MntSweetProducts, NumCatalogPurchases, MntFishProducts, MntFruits, MntGoldProds)

Business Insights¶

  • Berdasarkan hasil analisa yang dilakukan dari feature importance dengan menggunakan adaboost modeling dan setelah dilakukan hyper parameter tuning untuk proporsi dari segmentasi customer RFM cat, kategori loyal customer menjadi proporsi terbanyak berada di angka 829 dengan presentase 40% dari total jumlah customer
  • Dari feature education, tingkat pendidikan graduation mendapatkan proporsi tertinggi sebesar 1095 customer (53%) dari dari total jumlah customer
  • Response campaign berdasarkan tingkat pendidikan, customer dengan tingkat pendidikan Phd memberikan response paling banyak dari campaign yang diberikan dengan presentase sebesar 21% Namun tingkat pendidikan ini hanya mencerminkan 22% dari proporsi total customer sehingga kurang memiliki dampak.
  • Response campaign berdasarkan Rfm Category, customer dengan kategori champions menjadi customer yang paling banyak memberikan response terhadap campaign dengan presentase sebesar 34%
  • Dilakukan filterisasi data untuk mengfokuskan pada segmentasi tingkat pendidikan graduation dengan kategori loyal customer , segmentasi pendidikan graduation dipilih dikarenakan proporsinya paling banyak (53%). Sehingga dapat memberikan dampak paling besar pada hasil modeling.

Dilakukan perhitungan confidence interval dengan tingkat kepercayaan 95% dan nilai rata-rata dari cutomer dengan tingkat pendidikan graduation. Confidence interval memberikan gambaran tentang seberapa pasti kita dengan nilai rata-rata yang diestimasi dari sampel data. Berikut insight yang didapatkan :

  • Rata-rata periode sejak pembelian terakhir (Recency) adalah 32.72 hingga 46.01 hari. Ini menunjukkan bahwa sebagian pelanggan cukup aktif melakukan pembelian ulang.

  • Dapat disimpulkan bahwa dengan tingkat kepercayaan 95%, kita dapat yakin bahwa rata-rata rentang lifespan customer graduation berada di antara 393.45 dan 494.46 hari.

Pada hasil algoritma apriori melalui chanel pembelanjaan website dan catalogue dapat dilihat keterkaitan dan frekuensi untuk item yang dibeli secara bersamaan, fish product menjadi item yang sering dibeli dengan nilai support 89%. Dan ada beberapa item yang dibeli secara bersamaan melalui chanel penjualan.

  • Customer yang melakukan pembelian melalui chanel website dan catalogue cenderung juga membeli produk tertentu seperti MntFishProducts, MntFruits, MntGoldProds, dan MntSweetProducts

  • Informasi dari output apriori pada chanel pembelian bisa digunakan untuk placement produk apa saja yang akan di jual di website dan catalogue membuat paket penawaran yang mencakup item-item yang cenderung dibeli secara bersamaan (bundling).

Dengan strategi ini, perusahaan dapat meningkatkan penjualan lintas produk dan memberikan pengalaman berbelanja yang lebih terpersonalisasi kepada pelanggan.

Business Recommendation¶

  • Memberikan diskon harga pada produk terpilih (produk emas, ikan, makanan manis atau buah) untuk pembelian melalui website & katalog dengan tujuan meningkatkan ketertarikan pelanggan dan peningkatan penjualan.
  • Memberikan diskon harga pada produk terpilih (produk emas, ikan, makanan manis atau buah) untuk pembelian melalui website & katalog dengan tujuan meningkatkan ketertarikan pelanggan dan peningkatan penjualan.
  • Memberikan diskon harga pada produk terpilih (produk emas, ikan, makanan manis atau buah) untuk pembelian melalui website & katalog dengan tujuan meningkatkan ketertarikan pelanggan dan peningkatan penjualan.